@article{Monte_Carlo_2,
author = {Sylvain Arlot and Alain Celisse},
title = {{A survey of cross-validation procedures for model selection}},
volume = {4},
journal = {Statistics Surveys},
number = {none},
publisher = {Amer. Statist. Assoc., the Bernoulli Soc., the Inst. Math. Statist., and the Statist. Soc. Canada},
pages = {40 -- 79},
keywords = {cross-validation, leave-one-out, Model selection},
year = {2010},
doi = {10.1214/09-SS054},
URL = {https://doi.org/10.1214/09-SS054}
}

@misc{bohlender2021survey,
      title={A survey on shape-constraint deep learning for medical image segmentation}, 
      author={Simon Bohlender and Ilkay Oksuz and Anirban Mukhopadhyay},
      year={2021},
      eprint={2101.07721},
      archivePrefix={arXiv},
      primaryClass={eess.IV}
}

@misc{jurdi2020highlevel,
      title={High-level Prior-based Loss Functions for Medical Image Segmentation: A Survey}, 
      author={El Jurdi, Rosana  and Petitjean, Caroline  and Honeine, Paul  and Cheplygina, Veronika  and Abdallah, Fahed },
      year={2020},
      eprint={2011.08018},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}


@article{FCNN_Dolz,
  author    = {Jose Dolz and
               Christian Desrosiers and
               Ismail Ben Ayed},
  title     = {3D fully convolutional networks for subcortical segmentation in {MRI:}
               {A} large-scale study},
  journal   = {NeuroImage},
  volume    = {170},
  pages     = {456--470},
  year      = {2018},
  url       = {https://doi.org/10.1016/j.neuroimage.2017.04.039},
  doi       = {10.1016/j.neuroimage.2017.04.039},
  timestamp = {Wed, 14 Nov 2018 10:25:56 +0100},
  biburl    = {https://dblp.org/rec/journals/neuroimage/DolzDA18.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}


@article{CoordConv_other,
  author    = {Wenjia Wang and
               Junxuan Chen and
               Jie Zhao and
               Ying Chi and
               Xuansong Xie and
               Li Zhang and
               Xian{-}Sheng Hua},
  title     = {Automated Segmentation of Pulmonary Lobes using Coordination-Guided
               Deep Neural Networks},
  journal   = {CoRR},
  volume    = {abs/1904.09106},
  year      = {2019},
  url       = {http://arxiv.org/abs/1904.09106},
  archivePrefix = {arXiv},
  eprint    = {1904.09106},
  timestamp = {Thu, 23 Apr 2020 16:46:43 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1904-09106.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
@ARTICLE{Dice_Coeff2006,
author={W. R. {Crum} and O. {Camara} and D. L. G. {Hill}},
journal={IEEE Transactions on Medical Imaging},
title={Generalized Overlap Measures for Evaluation and Validation in Medical Image Analysis},
year={2006},
volume={25},
number={11},
pages={1451-1461},
keywords={biomedical MRI;brain;fuzzy set theory;image registration;image segmentation;medical image processing;neurophysiology;generalized overlap measures;medical image analysis;Dice coefficient;Tanimoto coefficient;image registration;segmentation algorithms;multiple test images;fractional labels;fuzzy set theory;error measure;overlap distance;Hausdorff distance;grey level images;nonrigid registration;three-dimensional magnetic resonance imaging;brain images;pragmatic segmentation;ground truth;magnetic resonance atlas brain;Biomedical imaging;Image analysis;Image segmentation;Magnetic resonance imaging;Image registration;Testing;Fuzzy set theory;Magnetic analysis;Brain;Magnetic resonance;Fuzzy sets;Hausdorff distance;morphological operations;registration;segmentation;validation;Algorithms;Brain;Humans;Image Enhancement;Image Interpretation, Computer-Assisted;Information Storage and Retrieval;Magnetic Resonance Imaging;Magnetic Resonance Imaging;Pattern Recognition, Automated;Pattern Recognition, Automated;Quality Assurance, Health Care;Quality Assurance, Health Care;Reproducibility of Results;Sensitivity and Specificity;Subtraction Technique},
doi={10.1109/TMI.2006.880587},
ISSN={1558-254X},
}


@Article{Guo2018,
author="Guo, Yanming
and Liu, Yu
and Georgiou, Theodoros
and Lew, Michael S.",
title="A review of semantic segmentation using deep neural networks",
journal="International Journal of Multimedia Information Retrieval",
year="2018",
month="Jun",
day="01",
volume="7",
number="2",
pages="87--93",
abstract="During the long history of computer vision, one of the grand challenges has been semantic segmentation which is the ability to segment an unknown image into different parts and objects (e.g., beach, ocean, sun, dog, swimmer). Furthermore, segmentation is even deeper than object recognition because recognition is not necessary for segmentation. Specifically, humans can perform image segmentation without even knowing what the objects are (for example, in satellite imagery or medical X-ray scans, there may be several objects which are unknown, but they can still be segmented within the image typically for further investigation). Performing segmentation without knowing the exact identity of all objects in the scene is an important part of our visual understanding process which can give us a powerful model to understand the world and also be used to improve or augment existing computer vision techniques. Herein this work, we review the field of semantic segmentation as pertaining to deep convolutional neural networks. We provide comprehensive coverage of the top approaches and summarize the strengths, weaknesses and major challenges.",
issn="2192-662X",
doi="10.1007/s13735-017-0141-z",

}
@incollection{Ciresan2012,
title = {Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images},
author = {Dan Ciresan and Alessandro Giusti and Luca M. Gambardella and Schmidhuber, J\"{u}rgen},
booktitle = {Advances in Neural Information Processing Systems 25},
editor = {F. Pereira and C. J. C. Burges and L. Bottou and K. Q. Weinberger},
pages = {2843--2851},
year = {2012},
publisher = {Curran Associates, Inc.},

}
%  url = {http://papers.nips.cc/paper/4741-deep-neural-networks-segment-neuronal-membranes-in-electron-microscopy-images.pdf}

@INPROCEEDINGS {8099664,
author = {A. Khoreva and R. Benenson and J. Hosang and M. Hein and B. Schiele},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
title = {Simple Does It: Weakly Supervised Instance and Semantic Segmentation},
year = {2017},
volume = {},
issn = {1063-6919},
pages = {1665-1674},
keywords = {training;semantics;labeling;image segmentation;proposals;shape},
doi = {10.1109/CVPR.2017.181},
month = {jul}
}
% url = {https://doi.ieeecomputersociety.org/10.1109/CVPR.2017.181},


  % URL = { https://hal-normandie-univ.archives-ouvertes.fr/hal-02183031 },



@INPROCEEDINGS{zhang20,
  author = {J. Zhang and C. Petitjean and S. Ainouz},
  title = {Kappa loss for skin lesion segmentation in fully convolutional network},
  booktitle = {{IEEE ISBI, Iowa City USA}},
    pages={2001-2004},
  year = {2020}
}



%   url = 	 {http://proceedings.mlr.press/v102/kervadec19a.html},
@InProceedings{Ravishankar2017,
author="Ravishankar, H.
and Venkataramani, R.
and Thiruvenkadam, S.
and Sudhakar, P.
and Vaidya, V.",
title="Learning and Incorporating Shape Models for Semantic Segmentation",
booktitle="MICCAI",
year="2017",
pages="203--211",
abstract="Semantic segmentation has been popularly addressed using Fully convolutional networks (FCN) (e.g. U-Net) with impressive results and has been the forerunner in recent segmentation challenges. However, FCN approaches do not necessarily incorporate local geometry such as smoothness and shape, whereas traditional image analysis techniques have benefitted greatly by them in solving segmentation and tracking problems. In this work, we address the problem of incorporating shape priors within the FCN segmentation framework. We demonstrate the utility of such a shape prior in robust handling of scenarios such as loss of contrast and artifacts. Our experiments show {\$}{\$}{\backslash}approx 5{\backslash}{\%}{\$}{\$}improvement over U-Net for the challenging problem of ultrasound kidney segmentation.",
isbn="978-3-319-66182-7"
}

@article{CHEPLYGINA2019280,
title = "Not-so-supervised: A survey of semi-supervised, multi-instance, and transfer learning in medical image analysis",
journal = "Medical Image Analysis",
volume = "54",
pages = "280 - 296",
year = "2019",
issn = "1361-8415",
doi = "https://doi.org/10.1016/j.media.2019.03.009",
url = "http://www.sciencedirect.com/science/article/pii/S1361841518307588",
author = "Veronika Cheplygina and Marleen [de Bruijne] and Josien P.W. Pluim",
keywords = "Machine learning, Medical imaging, Computer aided diagnosis, Semi-supervised learning, Weakly-supervised learning, Multiple instance learning, Transfer learning, Multi-task learning",
abstract = "Machine learning (ML) algorithms have made a tremendous impact in the field of medical imaging. While medical imaging datasets have been growing in size, a challenge for supervised ML algorithms that is frequently mentioned is the lack of annotated data. As a result, various methods that can learn with less/other types of supervision, have been proposed. We give an overview of semi-supervised, multiple instance, and transfer learning in medical imaging, both in diagnosis or segmentation tasks. We also discuss connections between these learning scenarios, and opportunities for future research. A dataset with the details of the surveyed papers is available via https://figshare.com/articles/Database_of_surveyed_literature_in_Not-so-supervised_a_survey_of_semi-supervised_multi-instance_and_transfer_learning_in_medical_image_analysis_/7479416."
}

@INPROCEEDINGS{3dunet2018,
  author={J. {Chang} and X. {Zhang} and J. {Chang} and M. {Ye} and D. {Huang} and P. {Wang} and C. {Yao}},
  booktitle={2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)}, 
  title={Brain Tumor Segmentation Based on 3D Unet with Multi-Class Focal Loss}, 
  year={2018},
  volume={},
  number={},
  pages={1-5},}



@article{Ghosh2016,
 author = {Ghosh, Payel and Mitchell, Melanie and Tanyi, James A. and Hung, Arthur Y.},
 title = {Incorporating Priors for Medical Image Segmentation Using a Genetic Algorithm},
 journal = {Neurocomput.},
 issue_date = {June 2016},
 volume = {195},
 number = {C},
 month = jun,
 year = {2016},
 issn = {0925-2312},
 pages = {181--194},
 numpages = {14},
 doi = {10.1016/j.neucom.2015.09.123},
 acmid = {2937546},
 publisher = {Elsevier Science Publishers B. V.},
 address = {Amsterdam, The Netherlands, The Netherlands},
 keywords = {Genetic Algorithms, Medical image segmentation, Relative position priors, Shape Modeling, Texture Analysis},
} 

@incollection{RFCN2016,
title = {R-FCN: Object Detection via Region-based Fully Convolutional Networks},
author = {Dai, Jifeng and Li, Yi and He, Kaiming and Sun, Jian},
booktitle = {Advances in Neural Information Processing Systems 29},
editor = {D. D. Lee and M. Sugiyama and U. V. Luxburg and I. Guyon and R. Garnett},
pages = {379--387},
year = {2016},
}
@INPROCEEDINGS {MaskRCNN,
author = {K. He and G. Gkioxari and P. Dollar and R. Girshick},
booktitle = {2017 IEEE International Conference on Computer Vision (ICCV)},
title = {Mask R-CNN},
year = {2017},
volume = {},
issn = {2380-7504},
pages = {2980-2988},
keywords = {feature extraction;image segmentation;object detection;semantics;quantization (signal);robustness},
doi = {10.1109/ICCV.2017.322},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
month = {oct}
}
% url = {https://doi.ieeecomputersociety.org/10.1109/ICCV.2017.322},



 % url = {https://doi.org/10.1109/TPAMI.2016.2572683},

@article{Chieh2014,
  title={Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs},
  author={Liang-Chieh Chen and George Papandreou and Iasonas Kokkinos and Kevin Murphy and Alan L. Yuille},
  journal={CoRR},
  year={2014},
  volume={abs/1412.7062}
}


@article{Lin15,
  title={Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation},
  author={Guosheng Lin and Chunhua Shen and Ian D. Reid and Anton van den Hengel},
  journal={2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2015},
  pages={3194-3203}
}



@inproceedings{NohHH15,
  author    = {Wenbin Yang and
               Quan Zhou and
               Jingnan Lu and
               Xiaofu Wu and
               Suofei Zhang and
               Longin Jan Latecki},
  title     = {Dense Deconvolutional Network for Semantic Segmentation},
  booktitle = {{IEEE} International Conference on Image Processing, {ICIP} ,
               Athens, Greece},
  pages     = {1573--1577},
  year      = {2018},
  doi       = {10.1109/ICIP.2018.8451256},
  timestamp = {Wed, 12 Dec 2018 16:25:50 +0100},
  biburl    = {https://dblp.org/rec/bib/conf/icip/YangZLWZL18},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
  % url       = {https://doi.org/10.1109/ICIP.2018.8451256},
@inproceedings{Chaudry2017,
  author    = {Arslan Chaudhry and
               Puneet Kumar Dokania and
               Philip H. S. Torr},
  title     = {Discovering Class-Specific Pixels for Weakly-Supervised Semantic Segmentation},
  booktitle = {British Machine Vision Conference 2017, {BMVC} 2017, London, UK, September
               4-7, 2017},
  year      = {2017},
  crossref  = {DBLP:conf/bmvc/2017},
  url       = {https://www.dropbox.com/s/adtejg8pvxrkbn9/0249.pdf?dl=1},
  timestamp = {Thu, 13 Sep 2018 13:28:13 +0200},
  biburl    = {https://dblp.org/rec/bib/conf/bmvc/ChaudhryDT17},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
@proceedings{DBLP:conf/bmvc/2017,
  title     = {British Machine Vision Conference 2017, {BMVC} 2017, London, UK, September
               4-7, 2017},
  publisher = {{BMVA} Press},
  year      = {2017},
  url       = {https://bmvc2017.london/programme-1/},
  timestamp = {Thu, 13 Sep 2018 13:27:45 +0200},
  biburl    = {https://dblp.org/rec/bib/conf/bmvc/2017},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
@INPROCEEDINGS{Pedro2015,
author={P. O. {Pinheiro} and R. {Collobert}},
booktitle={CVPR},
title={From image-level to pixel-level labeling with Convolutional Networks},
year={2015},
volume={},
number={},
pages={1713-1721},
keywords={convolution;image classification;image segmentation;learning (artificial intelligence);minimisation;neural nets;object tracking;smoothing methods;image-level labeling;pixel-level labeling;convolutional neural network;object segmentation;minimal prior;smoothing prior;multiple instance learning;MIL framework;image classification;Image segmentation;Training;Smoothing methods;Data models;Computational modeling;Feature extraction;Object segmentation},
doi={10.1109/CVPR.2015.7298780},
ISSN={},
}

@INPROCEEDINGS {BoxSup2015,
author = {J. Dai and K. He and J. Sun},
booktitle = {IEEE International Conference on Computer Vision (ICCV)},
title = {BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation},
year = {2015},
volume = {},
issn = {2380-7504},
pages = {1635-1643},
keywords = {training;image segmentation;semantics;proposals;labeling;linear programming;erbium},
doi = {10.1109/ICCV.2015.191},
address = {Los Alamitos, CA, USA},
month = {dec}
}

% url = {https://doi.ieeecomputersociety.org/10.1109/ICCV.2015.191},

@InProceedings{Li2010,
author="Li, Gang
and Wan, Youchuan",
editor="Zhu, Rongbo
and Zhang, Yanchun
and Liu, Baoxiang
and Liu, Chunfeng",
title="Adaptive Seeded Region Growing for Image Segmentation Based on Edge Detection, Texture Extraction and Cloud Model",
booktitle="Information Computing and Applications",
year="2010",
pages="285--292",
abstract="Considering the segmentation results of region growing depend on two key factors: seed selection and growing strategy, this paper proposed a method of adaptive seeded region growing based on edge detection, texture extraction and cloud model. Our work included two aspects. Firstly, we proposed a new method to extract region seeds automatically based on spectrum features, edge information and texture features. According to two conditions defined by us, region seeds could be extracted as accurately as possible. Secondly, we proposed an adaptive region growing strategy based on cloud model. Our strategy consisted of three major stages: expressing region by cloud model, calculating the qualitative region concept based on the backward cloud generator, and region growing based on cloud synthesis. The experiment results demonstrate seed extraction based on spectrum features, edge information and texture features has a good accuracy, and almost all of the extracted seeds are located at the homogeneous objects inner. The experiment results also demonstrate the adaptive region growing strategy based on cloud model makes regions grow not only in a simultaneous way but also with inner homogeneity.",
isbn="978-3-642-16167-4"
}


@INPROCEEDINGS{Pohle2001,
  author = {{Pohle}, R. and {Toennies}, K.~D.},
    title = "{Segmentation of medical images using adaptive region growing}",
booktitle = {Medical Imaging 2001: Image Processing},
     year = 2001,
  series = {\procspie},
  volume = 4322,
  editor = {{Sonka}, M. and {Hanson}, K.~M.},
    month = jul,
    pages = {1337-1346},
      doi = {10.1117/12.431013},
 
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
%   adsurl = {https://ui.adsabs.harvard.edu/abs/2001SPIE.4322.1337P},
@INPROCEEDINGS {DCAN,
author = {H. Chen and X. Qi and L. Yu and P. Heng},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
title = {DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation},
year = {2016},
volume = {},
issn = {1063-6919},
pages = {2487-2496},
keywords = {glands;image segmentation;cancer;training;feature extraction;neural networks;biomedical imaging},
doi = {10.1109/CVPR.2016.273},
month = {jun}
}





@inproceedings{Mirikharaji2018,
  author    = {Zahra Mirikharaji and
               Ghassan Hamarneh},
  title     = {Star Shape Prior in Fully Convolutional Networks for Skin Lesion Segmentation},
  booktitle = {MICCAI},
  volume    = {11073},
  pages     = {737--745},
  year      = {2018},
  doi       = {10.1007/978-3-030-00937-3\_84},
  timestamp = {Tue, 14 May 2019 10:00:50 +0200},
  biburl    = {https://dblp.org/rec/bib/conf/miccai/MirikharajiH18},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
  % url       = {https://doi.org/10.1007/978-3-030-00937-3\_84},

@INPROCEEDINGS{MASK-X-RCNN, 
author={R. {Hu} and P. {Dollár} and K. {He} and T. {Darrell} and R. {Girshick}}, 
booktitle={2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition}, 
title={Learning to Segment Every Thing}, 
year={2018}, 
volume={}, 
number={}, 
pages={4233-4241}, 
keywords={convolutional neural nets;image annotation;image segmentation;supervised learning;transfer functions;object instance segmentation;segmentation masks;partially supervised training paradigm;box annotations;mask annotations;Mask {R-CNN};COCO dataset;instance segmentation model training;Visual Genome dataset;weight transfer function;Training;Task analysis;Visualization;Transfer functions;Predictive models;Image segmentation;Genomics}, 
doi={10.1109/CVPR.2018.00445}, 
ISSN={2575-7075}, 
month={June},}





@InProceedings{ELJURDI2020_CoordConv,
  author    = {El Jurdi, Rosana and Dargent, Thomas and Petitjean, Caroline and Honeine, Paul and Abdallah, Fahed},
  title     = {Investigating CoordConv for Fully and Weakly Supervised Medical Image Segmentation},
  booktitle = {Tenth International Conference on Image Processing Theory, Tools and
               Applications, {IPTA} 2020, Paris, France},
 
  year      = {\noop{2020}Submitted},
  
}
@InProceedings{ELJURDI2019_GRETSI,
  TITLE = {{Organ Segmentation in CT Images With Weak Annotations: A Preliminary Study}},
  AUTHOR = {El Jurdi, Rosana and Petitjean, Caroline and Honeine, Paul and Abdallah, Fahed},
  BOOKTITLE = {{27th GRETSI Symposium on Signal and Image Processing}},
  ADDRESS = {Lille, France},
  YEAR = {2019},
  MONTH = Aug,
  PDF = { https://hal-normandie-univ.archives-ouvertes.fr/hal-02183031/file/19.gretsi.weak.pdf },
  HAL_ID = {hal-02183031},
  HAL_VERSION = {v1},
}


@InProceedings{ELJURDI2019_GDRISIS,
  TITLE = {{Towards Semi-supervised Segmentation of Organs at Risk Using Deep Convolutional Neural Networks}},
  AUTHOR = {El Jurdi, Rosana and Petitjean, Caroline and Honeine, Paul and Abdallah, Fahed},
  BOOKTITLE = {{DR-ISIS workshop: Apprentissage faiblement supervisé ou nonsupervisé pour l'analyse d'images et de video}},
  ADDRESS = {Paris, France},
  YEAR = {2019},
  MONTH = May,
  
}
% HAL_ID = {hal-02183031},
%  HAL_VERSION = {v1},
% PDF = { https://hal-normandie-univ.archives-ouvertes.fr/hal-02183031/file/19.gretsi.weak.pdf },
@ARTICLE{ELJURDI2020_BBUNET,
  author={R. {El Jurdi} and C. {Petitjean} and P. {Honeine} and F. {Abdallah}},
  journal={IEEE Journal of Selected Topics in Signal Processing},
  title={{BB-UNet: U-Net with Bounding Box Prior}}, 
  year={2020},
  volume={14},
  number={6},
  pages={1189 - 1198},}

@article{DIVE,
    author = {Fakhry, Ahmed and Peng, Hanchuan and Ji, Shuiwang},
    title = "{Deep models for brain EM image segmentation: novel insights and improved performance}",
    journal = {Bioinformatics},
    volume = {32},
    number = {15},
    pages = {2352-2358},
    year = {2016},
    month = {03},
    abstract = "{Motivation: Accurate segmentation of brain electron microscopy (EM) images is a critical step in dense circuit reconstruction. Although deep neural networks (DNNs) have been widely used in a number of applications in computer vision, most of these models that proved to be effective on image classification tasks cannot be applied directly to EM image segmentation, due to the different objectives of these tasks. As a result, it is desirable to develop an optimized architecture that uses the full power of DNNs and tailored specifically for EM image segmentation.Results: In this work, we proposed a novel design of DNNs for this task. We trained a pixel classifier that operates on raw pixel intensities with no preprocessing to generate probability values for each pixel being a membrane or not. Although the use of neural networks in image segmentation is not completely new, we developed novel insights and model architectures that allow us to achieve superior performance on EM image segmentation tasks. Our submission based on these insights to the 2D EM Image Segmentation Challenge achieved the best performance consistently across all the three evaluation metrics. This challenge is still ongoing and the results in this paper are as of June 5, 2015.Availability and Implementation: https://github.com/ahmed-fakhry/diveContact: sji@eecs.wsu.edu}",
    issn = {1367-4803},
    doi = {10.1093/bioinformatics/btw165},
   
   
}


%  url = {https://doi.org/10.1093/bioinformatics/btw165},
% eprint = {https://academic.oup.com/bioinformatics/article-pdf/32/15/2352/9642486/btw165.pdf},

@InProceedings{3D-UNET,
  author       = "{\"O}. {\c{C}}i{\c{c}}ek and A. Abdulkadir and S.S. Lienkamp and T. Brox and O. Ronneberger",
  title        = "3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation",
  booktitle    = "MICCAI",
  series       = "LNCS",
  volume       = "9901",
  pages        = "424--432",
  month        = "Oct",
  year         = "2016",
  keywords     = "Convolutional Neural Networks, 3D, Biomedical Volumetric Image Segmentation, Xenopus Kidney, Semi-automated, Fully-automated, Sparse Annotation",
  url          = "http://lmb.informatik.uni-freiburg.de/Publications/2016/CABR16"
}


@InProceedings{Unet,
author="Ronneberger, Olaf
and Fischer, Philipp
and Brox, Thomas",
title="U-Net: Convolutional Networks for Biomedical Image Segmentation",
booktitle="MICCAI",
year="2015",
pages="234--241",
abstract="There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise \textcolor{blue}{localization}\textcolor{red}{Here meaning fits the term since Unet localizes information}. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net.",
isbn="978-3-319-24574-4"
}
@INPROCEEDINGS{WILDCAT, 
author={T. {Durand} and T. {Mordan} and N. {Thome} and M. {Cord}}, 
booktitle={2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, 
title={{WILDCAT}: Weakly Supervised Learning of Deep ConvNets for Image Classification, Pointwise Localization and Segmentation}, 
year={2017}, 
volume={}, 
number={}, 
pages={5957-5966}, 
keywords={image classification;image segmentation;neural nets;object detection;object recognition;convolutional neural networks;fully convolutional networks;image segmentation;image region alignment;global image labels;spatial invariance;deep learning method;pointwise localization;deep ConvNets;weakly supervised learning;state-of-the-art methods;weakly supervised training;global image prediction;WILDCAT;semantic segmentation;weakly supervised object localization;image classification;Image segmentation;Semantics;Dogs;Object detection;Head;Legged locomotion;Feature extraction}, 
doi={10.1109/CVPR.2017.631}, 
ISSN={1063-6919}, 
month={July},}


@ARTICLE{NAS-UNET, 
author={Y. {Weng} and T. {Zhou} and Y. {Li} and X. {Qiu}}, 
journal={IEEE Access}, 
title={NAS-Unet: Neural Architecture Search for Medical Image Segmentation}, 
year={2019}, 
volume={7}, 
number={}, 
pages={44247-44257}, 
keywords={biomedical MRI;biomedical ultrasonics;computerised tomography;image classification;image segmentation;medical image processing;neural net architecture;NAS-Unet;neural architecture search;medical image segmentation;image classification;semantic image segmentation;U-net architecture;magnetic resonance imaging;medical image datasets;cell architecture;DownSC;UpSC;Promise12 datasets;Chaos datasets;ultrasound nerve datasets;computed tomography;U-like backbone network;U-net;Computer architecture;Image segmentation;Magnetic resonance imaging;Medical diagnostic imaging;Task analysis;Microprocessors;Medical image segmentation;convolutional neural architecture search;deep learning}, 
doi={10.1109/ACCESS.2019.2908991}, 
ISSN={2169-3536}, 
month={},}

@ARTICLE{H-DenseUNet, 
author={X. {Li} and H. {Chen} and X. {Qi} and Q. {Dou} and C. {Fu} and P. {Heng}}, 
journal={IEEE Transactions on Medical Imaging}, 
title={H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes}, 
year={2018}, 
volume={37}, 
number={12}, 
pages={2663-2674}, 
keywords={cancer;computerised tomography;convolution;feedforward neural nets;graphics processing units;image segmentation;learning (artificial intelligence);liver;medical image processing;patient diagnosis;patient treatment;tumours;liver segmentation;H-DenseUNet;hybrid densely connected UNet;CT volumes;hepatocellular carcinoma diagnosis;treatment planning;tumor segmentation method;fully convolutional neural networks;FCNs;GPU memory consumption;2-D DenseUNet;intra-slice features;end-to-end manner;inter-slice features;liver cancer;image segmentation;feature fusion layer;backbone;learning process;Liver;Three-dimensional displays;Two dimensional displays;Image segmentation;Feature extraction;Lesions;CT;liver tumor segmentation;deep learning;hybrid features}, 
doi={10.1109/TMI.2018.2845918}, 
ISSN={0278-0062}, 
month={Dec},}
@article{Mostafa2018,
  author    = {Mostafa S. Ibrahim and
               Arash Vahdat and
               William G. Macready},
  title     = {Weakly Supervised Semantic Image Segmentation with Self-correcting
               Networks},
  journal   = {CoRR},
  volume    = {abs/1811.07073},
  year      = {2018},
  archivePrefix = {arXiv},
  eprint    = {1811.07073},
  timestamp = {Sun, 25 Nov 2018 18:57:12 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1811-07073.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@INPROCEEDINGS{SegResNet,

  author={Fausto Milletari and Nassir Navab and Seyed-Ahmad Ahmadi},
  booktitle={2016 Fourth International Conference on 3D Vision (3DV)}, 
 title={V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation}, 
  year={2016},
  volume={},
  number={},
  pages={565-571},
  abstract={Convolutional Neural Networks (CNNs) have been recently employed to solve problems from both the computer vision and medical image analysis fields. Despite their popularity, most approaches are only able to process 2D images while most medical data used in clinical practice consists of 3D volumes. In this work we propose an approach to 3D image segmentation based on a volumetric, fully convolutional, neural network. Our CNN is trained end-to-end on MRI volumes depicting prostate, and learns to predict segmentation for the whole volume at once. We introduce a novel objective function, that we optimise during training, based on Dice coefficient. In this way we can deal with situations where there is a strong imbalance between the number of foreground and background voxels. To cope with the limited number of annotated volumes available for training, we augment the data applying random non-linear transformations and histogram matching. We show in our experimental evaluation that our approach achieves good performances on challenging test data while requiring only a fraction of the processing time needed by other previous methods.},
  keywords={biomedical MRI;image segmentation;medical image processing;neural nets;V-Net;fully convolutional neural networks;volumetric medical image segmentation;CNN;computer vision;medical image analysis;clinical practice;3D image segmentation;MRI volumes;magnetic resonance imaging;Dice coefficient;foreground voxel;background voxel;random nonlinear transformations;histogram matching;Image segmentation;Feature extraction;Biomedical imaging;Three-dimensional displays;Neural networks;Magnetic resonance imaging;Two dimensional displays;Deep learning;segmentation;prostate;machine learning;convolutional neural networks},
  doi={10.1109/3DV.2016.79},
  ISSN={},
  month={Oct},} 
@INPROCEEDINGS{V-NET, 
author={Fausto Milletari and Nassir Navab and Seyed-Ahmad Ahmadi}, 
booktitle={2016 Fourth International Conference on 3D Vision (3DV)}, 
title={V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation}, 
year={2016}, 
volume={}, 
number={}, 
pages={565-571}, 
keywords={biomedical MRI;image segmentation;medical image processing;neural nets;V-Net;fully convolutional neural networks;volumetric medical image segmentation;CNN;computer vision;medical image analysis;clinical practice;3D image segmentation;MRI volumes;magnetic resonance imaging;Dice coefficient;foreground voxel;background voxel;random nonlinear transformations;histogram matching;Image segmentation;Feature extraction;Biomedical imaging;Three-dimensional displays;Neural networks;Magnetic resonance imaging;Two dimensional displays;Deep learning;segmentation;prostate;machine learning;convolutional neural networks}, 
doi={10.1109/3DV.2016.79}, 
ISSN={}, 
month={Oct},}
@inproceedings{Reddy2019,
title={Brain Tumor Segmentation using Topological Loss in Convolutional Networks},
author={Charan Reddy and Karthik Gopinath and Herve Lombaert},
booktitle={MIDL},
address={London, UK},
year={2019},
month={08--10 Jul},
url={https://openreview.net/forum?id=Syxckl40tE},
abstract={Fully Convolutional Networks (FCNs) are widely used in medical image analysis for segmentation tasks.  However,  most FCNs fail to directly incorporate image geometry such as topology and boundary smoothness during segmentation.  The sub-regions of the brain tumor in MRI images follow a particular topological order. However, the automatic segmentation of these brain tumors with conventional FCNs may violate the topological structure of brain tumors.  FCNs could be constrained with a topological loss to enforce structured predictions.  This paper presents the effect of such topological loss on brain tumor segmentation using the BraTS dataset.},
}






@ARTICLE{Zotti2019,
author={C. {Zotti} and Z. {Luo} and A. {Lalande} and P. {Jodoin}},
journal={IEEE Journal of Biomedical and Health Informatics},
title={Convolutional Neural Network With Shape Prior Applied to Cardiac MRI Segmentation},
year={2019},
volume={23},
number={3},
pages={1119-1128},
keywords={biomedical MRI;cardiology;convolutional neural nets;image segmentation;medical image processing;cardiac MRI segmentation;endocardium segmentation;right ventricle contours;left ventricle contours;multislice CMRI segmentation;automatic cardiac diagnostic challenge;medical image computing;short-axis cardiac magnetic resonance slices;epicardium segmentation;multiresolution grid architecture;input raw magnetic resonance images;cardiac anatomy;convolutional neural network architecture;time 0.18 s;distance 9.5 mm;Shape;Image segmentation;Magnetic resonance imaging;Informatics;Three-dimensional displays;Machine learning;Two dimensional displays;Cardiac MRI segmentation;convolutional neural networks;shape prior},

ISSN={},
month={May},}
% doi={10.1109/JBHI.2018.2865450},

@ARTICLE{Gibson2018,
author={E. {Gibson} and F. {Giganti} and Y. {Hu} and E. {Bonmati} and S. {Bandula} and K. {Gurusamy} and B. {Davidson} and S. P. {Pereira} and M. J. {Clarkson} and D. C. {Barratt}},
journal={IEEE Transactions on Medical Imaging},
title={Automatic Multi-Organ Segmentation on Abdominal CT With Dense V-Networks},
year={2018},
volume={37},
number={8},
pages={1822-1834},
keywords={biological organs;biomedical MRI;computerised tomography;endoscopes;image fusion;image registration;image segmentation;kidney;learning (artificial intelligence);liver;medical image processing;statistical analysis;image-guided navigation;multiorgan abdominal CT segmentation;registration-free method;multicentre data;existing deep learning;endoscopic pancreatic procedures;registration-free deep-learning-based segmentation algorithm;abdominal organs;alternative methods;abdominal images;inter-subject image registrations;statistical models;segmentation methods;computed tomography images;abdominal anatomy;automatic segmentation;automatic multiorgan segmentation;Image segmentation;Computed tomography;Liver;Kidney;Three-dimensional displays;Pancreas;Abdominal CT;segmentation;deep learning;pancreas;gastrointestinal tract;stomach;duodenum;esophagus;liver;spleen;kidney;gallbladder},
ISSN={},
month={Aug},}


% doi={10.1109/TMI.2018.2806309},




@INPROCEEDINGS{NU-Net,
author={M. {Samy} and K. {Amer} and K. {Eissa} and M. {Shaker} and M. {ElHelw}},
booktitle={2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
title={NU-Net: Deep Residual Wide Field of View Convolutional Neural Network for Semantic Segmentation},
year={2018},
volume={},
number={},
pages={267-2674},
keywords={computer vision;convolutional neural nets;feature extraction;geophysical image processing;image classification;image segmentation;land cover;NU-Net;semantic segmentation;satellite images;computer vision;local information;encoder;pretrained classification model;input spatial size;predicted mask;conventional architecture;resolution information;deep FCNN;fully convolutional neural networks;land cover classification;deep residual wide field-of-view convolutional neural network;wide field-of-view global information;road extraction tracks;DeepGlobe competition;Image segmentation;Semantics;Roads;Computer architecture;Convolution;Computer vision;Pattern recognition},
doi={10.1109/CVPRW.2018.00050},
ISSN={},
month={June},}



@INPROCEEDINGS{StachedU-Net,
author={A. {Ghosh} and M. {Ehrlich} and S. {Shah} and L. {Davis} and R. {Chellappa}},
booktitle={2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
title={Stacked U-Nets for Ground Material Segmentation in Remote Sensing Imagery},
year={2018},
volume={},
number={},
pages={252-2524},
keywords={geophysical image processing;image colour analysis;image segmentation;remote sensing;RGB remote sensing images;state-of-the-art method;additional post-processing;image tiles;ground material segmentation;remote sensing imagery;semantic segmentation algorithm;dilated stacked U-Nets architecture;DeepGlobe dataset;Semantics;Image segmentation;Remote sensing;Convolution;Training;Satellites;Computer architecture},
doi={10.1109/CVPRW.2018.00047},
ISSN={},
month={June},}


@INPROCEEDINGS{StackedU-Net2,
author={T. {Sun} and Z. {Chen} and W. {Yang} and Y. {Wang}},
booktitle={Computer Vision and Pattern Recognition Workshops (CVPRW)},
title={Stacked U-Nets with Multi-output for Road Extraction},
year={2018},
volume={},
number={},
pages={187-1874},
keywords={computer vision;feature extraction;feedforward neural nets;learning (artificial intelligence);roads;traffic engineering computing;road extraction;computer vision;satellite imagery;multiple output;hybrid loss function;post-processing methods;road map vectorization;shortest path search;vanilla VGG network;stacked u-nets;convolutional neural networks;Roads;Decoding;Computer vision;Encoding;Feature extraction;Neural networks;Satellites},
doi={10.1109/CVPRW.2018.00033},
ISSN={},
month={June},}


@ARTICLE{Jia2017,
author={Z. {Jia} and X. {Huang} and E. I. {Chang} and Y. {Xu}},
journal={IEEE Transactions on Medical Imaging},
title={Constrained Deep Weak Supervision for Histopathology Image Segmentation},
year={2017},
volume={36},
number={11},
pages={2376-2388},
keywords={cancer;image segmentation;learning (artificial intelligence);medical image processing;neural nets;constrained deep weak supervision;histopathology image segmentation;cancerous regions;multiple instance learning framework;MIL framework;neural networks;end-to-end learning system;fully convolutional networks;DWS-MIL;Image segmentation;Supervised learning;Biomedical imaging;Cancer;Training;Neural networks;Prediction algorithms;Convolutional neural networks;histopathology image segmentation;weakly supervised learning;fully convolutional networks;multiple instance learning;Algorithms;Colon;Colonic Neoplasms;Databases, Factual;Histocytochemistry;Humans;Image Processing, Computer-Assisted;Neural Networks (Computer);Supervised Machine Learning;Tissue Array Analysis},
doi={10.1109/TMI.2017.2724070},
ISSN={},
month={Nov},}


@inproceedings{VB-Net,
  author    = {Miaofei Han and
               Guang Yao and
               Wenhai Zhang and
               Guangrui Mu and
               Yiqiang Zhan and
               Xiang Zhou and
               Yaozong Gao},
  title     = {Segmentation of {CT} Thoracic Organs by Multi-resolution VB-nets},
  booktitle = {Proceedings of the 2019 Challenge on Segmentation of THoracic Organs
               at Risk in {CT} Images, SegTHOR@ISBI 2019, April 8, 2019},
  year      = {2019},
  crossref  = {DBLP:conf/isbi/2019segthor},
  
  timestamp = {Tue, 28 May 2019 16:23:47 +0200},
  biburl    = {https://dblp.org/rec/bib/conf/isbi/HanYZMZZG19},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
%  url       = {http://ceur-ws.org/Vol-2349/SegTHOR2019\_paper\_1.pdf},
@proceedings{DBLP:conf/isbi/2019segthor,
  editor    = {Caroline Petitjean and
               Su Ruan and
               Zo{\'{e}} Lambert and
               Bernard Dubray},
  title     = {Proceedings of the 2019 Challenge on Segmentation of THoracic Organs
               at Risk in {CT} Images, SegTHOR@ISBI 2019, April 8, 2019},
  series    = {{CEUR} Workshop Proceedings},
  volume    = {2349},
  publisher = {CEUR-WS.org},
  year      = {2019},
  url       = {http://ceur-ws.org/Vol-2349},
  urn       = {urn:nbn:de:0074-2349-2},
  timestamp = {Tue, 28 May 2019 16:23:47 +0200},
  biburl    = {https://dblp.org/rec/bib/conf/isbi/2019segthor},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}


@InProceedings{olga,
author="Veksler, Olga",
title="Star Shape Prior for Graph-Cut Image Segmentation",
booktitle="ECCV",
year="2008",
pages="454--467",
abstract="In recent years, segmentation with graph cuts is increasingly used for a variety of applications, such as photo/video editing, medical image processing, etc. One of the most common applications of graph cut segmentation is extracting an object of interest from its background. If there is any knowledge about the object shape (i.e. a shape prior), incorporating this knowledge helps to achieve a more robust segmentation. In this paper, we show how to implement a star shape prior into graph cut segmentation. This is a generic shape prior, i.e. it is not specific to any particular object, but rather applies to a wide class of objects, in particular to convex objects. Our major assumption is that the center of the star shape is known, for example, it can be provided by the user. The star shape prior has an additional important benefit - it allows an inclusion of a term in the objective function which encourages a longer object boundary. This helps to alleviate the bias of a graph cut towards shorter segmentation boundaries. In fact, we show that in many cases, with this new term we can achieve an accurate object segmentation with only a single pixel, the center of the object, provided by the user, which is rarely possible with standard graph cut interactive segmentation.",
isbn="978-3-540-88690-7"
}



%  url = "http://www.sciencedirect.com/science/article/pii/S1361841518306133",
@INPROCEEDINGS{Dalca2018,
author={A. V. {Dalca} and J. {Guttag} and M. R. {Sabuncu}},
booktitle={2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition},
title={Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentation},
year={2018},
volume={},
number={},
pages={9290-9299},
abstract={We consider the problem of segmenting a biomedical image into anatomical regions of interest. We specifically address the frequent scenario where we have no paired training data that contains images and their manual segmentations. Instead, we employ unpaired segmentation images that we use to build an anatomical prior. Critically these segmentations can be derived from imaging data from a different dataset and imaging modality than the current task. We introduce a generative probabilistic model that employs the learned prior through a convolutional neural network to compute segmentations in an unsupervised setting. We conducted an empirical analysis of the proposed approach in the context of structural brain MRI segmentation, using a multi-study dataset of more than 14,000 scans. Our results show that an anatomical prior enables fast unsupervised segmentation which is typically not possible using standard convolutional networks. The integration of anatomical priors can facilitate CNN-based anatomical segmentation in a range of novel clinical problems, where few or no annotations are available and thus standard networks are not trainable. The code, model definitions and model weights are freely available at http://github.com/adalca/neuron.},
keywords={biomedical MRI;brain;feedforward neural nets;image segmentation;medical image processing;neurophysiology;unsupervised learning;unsupervised biomedical segmentation;biomedical image;manual segmentations;unpaired segmentation images;imaging modality;generative probabilistic model;convolutional neural network;unsupervised setting;structural brain MRI segmentation;multistudy dataset;fast unsupervised segmentation;standard convolutional networks;anatomical priors;CNN-based anatomical segmentation;clinical problems;standard networks;empirical analysis;Image segmentation;Biological system modeling;Biomedical imaging;Convolutional neural networks;Shape;Computational modeling;Decoding},
doi={10.1109/CVPR.2018.00968},
ISSN={},
month={June},}


@ARTICLE{CHen2018,
author={L. {Chen} and G. {Papandreou} and I. {Kokkinos} and K. {Murphy} and A. L. {Yuille}},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
title={DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs},
year={2018},
volume={40},
number={4},
pages={834-848},
abstract={In this work we address the task of semantic image segmentation with Deep Learning and make three main contributions that are experimentally shown to have substantial practical merit. First, we highlight convolution with upsampled filters, or `atrous convolution', as a powerful tool in dense prediction tasks. Atrous convolution allows us to explicitly control the resolution at which feature responses are computed within Deep Convolutional Neural Networks. It also allows us to effectively enlarge the field of view of filters to incorporate larger context without increasing the number of parameters or the amount of computation. Second, we propose atrous spatial pyramid pooling (ASPP) to robustly segment objects at multiple scales. ASPP probes an incoming convolutional feature layer with filters at multiple sampling rates and effective fields-of-views, thus capturing objects as well as image context at multiple scales. Third, we improve the localization of object boundaries by combining methods from DCNNs and probabilistic graphical models. The commonly deployed combination of max-pooling and downsampling in DCNNs achieves invariance but has a toll on localization accuracy. We overcome this by combining the responses at the final DCNN layer with a fully connected Conditional Random Field (CRF), which is shown both qualitatively and quantitatively to improve localization performance. Our proposed “DeepLab” system sets the new state-of-art at the PASCAL VOC-2012 semantic image segmentation task, reaching 79.7 percent mIOU in the test set, and advances the results on three other datasets: PASCAL-Context, PASCAL-Person-Part, and Cityscapes. All of our code is made publicly available online.},
keywords={convolution;feature extraction;feedforward neural nets;image segmentation;learning (artificial intelligence);random processes;highlight convolution;atrous convolution;Deep Convolutional Neural Networks;atrous spatial pyramid pooling;image context;fully connected Conditional Random Field;PASCAL VOC-2012 semantic image segmentation task;deep convolutional nets;Deep Learning;DeepLab;semantic image segmentation;probabilistic graphical models;Convolution;Image segmentation;Semantics;Image resolution;Computational modeling;Neural networks;Context;Convolutional neural networks;semantic segmentation;atrous convolution;conditional random fields},
doi={10.1109/TPAMI.2017.2699184},
ISSN={},
month={April},}

@article{KERVADEC2019_SizeLoss,
title = "Constrained-CNN losses for weakly supervised segmentation",
journal = "Medical Image Analysis",
volume = "54",
pages = "88 - 99",
year = "2019",
issn = "1361-8415",
doi = "https://doi.org/10.1016/j.media.2019.02.009",

author = "Hoel Kervadec and Jose Dolz and Meng Tang and Eric Granger and Yuri Boykov and Ismail Ben Ayed",
keywords = "Deep learning, Semantic segmentation, Weakly-supervised learning, CNN constraints",
abstract = "Weakly-supervised learning based on, e.g., partially labelled images or image-tags, is currently attracting significant attention in CNN segmentation as it can mitigate the need for full and laborious pixel/voxel annotations. Enforcing high-order (global) inequality constraints on the network output (for instance, to constrain the size of the target region) can leverage unlabeled data, guiding the training process with domain-specific knowledge. Inequality constraints are very flexible because they do not assume exact prior knowledge. However, constrained Lagrangian dual optimization has been largely avoided in deep networks, mainly for computational tractability reasons. To the best of our knowledge, the method of Pathak et al. (2015a) is the only prior work that addresses deep CNNs with linear constraints in weakly supervised segmentation. It uses the constraints to synthesize fully-labeled training masks (proposals) from weak labels, mimicking full supervision and facilitating dual optimization. We propose to introduce a differentiable penalty, which enforces inequality constraints directly in the loss function, avoiding expensive Lagrangian dual iterates and proposal generation. From constrained-optimization perspective, our simple penalty-based approach is not optimal as there is no guarantee that the constraints are satisfied. However, surprisingly, it yields substantially better results than the Lagrangian-based constrained CNNs in Pathak et al. (2015a), while reducing the computational demand for training. By annotating only a small fraction of the pixels, the proposed approach can reach a level of segmentation performance that is comparable to full supervision on three separate tasks. While our experiments focused on basic linear constraints such as the target-region size and image tags, our framework can be easily extended to other non-linear constraints, e.g., invariant shape moments (Klodt and Cremers, 2011) and other region statistics (Lim et al., 2014). Therefore, it has the potential to close the gap between weakly and fully supervised learning in semantic medical image segmentation. Our code is publicly available."
}
%  url = "http://www.sciencedirect.com/science/article/pii/S1361841518306145",

@INPROCEEDINGS{Pathak2015,
author={D. {Pathak} and P. {Krähenbühl} and T. {Darrell}},
booktitle={ICCV},
title={Constrained Convolutional Neural Networks for Weakly Supervised Segmentation},
year={2015},
volume={},
number={},
pages={1796-1804},
abstract={We present an approach to learn a dense pixel-wise labeling from image-level tags. Each image-level tag imposes constraints on the output labeling of a Convolutional Neural Network (CNN) classifier. We propose Constrained CNN (CCNN), a method which uses a novel loss function to optimize for any set of linear constraints on the output space (i.e. predicted label distribution) of a CNN. Our loss formulation is easy to optimize and can be incorporated directly into standard stochastic gradient descent optimization. The key idea is to phrase the training objective as a biconvex optimization for linear models, which we then relax to nonlinear deep networks. Extensive experiments demonstrate the generality of our new learning framework. The constrained loss yields state-of-the-art results on weakly supervised semantic image segmentation. We further demonstrate that adding slightly more supervision can greatly improve the performance of the learning algorithm.},
keywords={image segmentation;learning (artificial intelligence);neural nets;constrained convolutional neural networks;dense pixel-wise labeling;image-level tags;CNN classifier;loss formulation;stochastic gradient descent optimization;learning framework;weakly supervised semantic image segmentation;Optimization;Image segmentation;Labeling;Neural networks;Standards;Semantics;Convolutional codes},
doi={10.1109/ICCV.2015.209},
ISSN={},
month={Dec},}

@INPROCEEDINGS{Papandrou2015,
author={G. {Papandreou} and L. {Chen} and K. P. {Murphy} and A. L. {Yuille}},
booktitle={2015 IEEE International Conference on Computer Vision (ICCV)},
title={Weakly-and Semi-Supervised Learning of a Deep Convolutional Network for Semantic Image Segmentation},
year={2015},
volume={},
number={},
pages={1742-1750},
abstract={Deep convolutional neural networks (DCNNs) trained on a large number of images with strong pixel-level annotations have recently significantly pushed the state-of-art in semantic image segmentation. We study the more challenging problem of learning DCNNs for semantic image segmentation from either (1) weakly annotated training data such as bounding boxes or image-level labels or (2) a combination of few strongly labeled and many weakly labeled images, sourced from one or multiple datasets. We develop Expectation-Maximization (EM) methods for semantic image segmentation model training under these weakly supervised and semi-supervised settings. Extensive experimental evaluation shows that the proposed techniques can learn models delivering competitive results on the challenging PASCAL VOC 2012 image segmentation benchmark, while requiring significantly less annotation effort. We share source code implementing the proposed system at https://bitbucket.org/deeplab/deeplab-public.},
keywords={expectation-maximisation algorithm;feedforward neural nets;image segmentation;learning (artificial intelligence);weakly-supervised learning;semisupervised learning;deep convolutional neural network;semantic image segmentation model training;DCNN;pixel-level annotation;weakly annotated training data;strongly labeled image;weakly labeled image;expectation-maximization method;EM method;PASCAL VOC 2012 image segmentation benchmark;Image segmentation;Training;Semantics;Benchmark testing;Training data;Convolutional codes;Google},
doi={10.1109/ICCV.2015.203},
ISSN={},
month={Dec},}

@InProceedings{Fang_2018,
author = {Fang, Hao-Shu and Lu, Guansong and Fang, Xiaolin and Xie, Jianwen and Tai, Yu-Wing and Lu, Cewu},
title = {Weakly and Semi Supervised Human Body Part Parsing via Pose-Guided Knowledge Transfer},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2018}
}


@INPROCEEDINGS {Khoreva16,
author = {A. Khoreva and R. Benenson and J. Hosang and M. Hein and B. Schiele},
booktitle = {CVPR},
title = {Simple Does It: Weakly Supervised Instance and Semantic Segmentation},
year = {2017},
volume = {},
issn = {1063-6919},
pages = {1665-1674},
keywords = {training;semantics;labeling;image segmentation;proposals;shape},
doi = {10.1109/CVPR.2017.181},

publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
month = {jul}
}
@article{MCG,
 author = {Pont-Tuset, Jordi and Arbelaez, Pablo and T.Barron, Jonathan and Marques, Ferran and Malik, Jitendra},
 title = {Multiscale Combinatorial Grouping for Image Segmentation and Object Proposal Generation},
 journal = {IEEE Trans. Pattern Anal. Mach. Intell.},
 issue_date = {January 2017},
 volume = {39},
 number = {1},
 month = jan,
 year = {2017},
 issn = {0162-8828},
 pages = {128--140},
 numpages = {13},
 
 doi = {10.1109/TPAMI.2016.2537320},
 acmid = {3024261},
 publisher = {IEEE Computer Society},
 address = {Washington, DC, USA},
} 
%  url = {https://doi.org/10.1109/TPAMI.2016.2537320},
@article{GRabCut2004,
 author = {Rother, Carsten and Kolmogorov, Vladimir and Blake, Andrew},
 title = {"GrabCut": Interactive Foreground Extraction Using Iterated Graph Cuts},
 journal = {ACM Trans. Graph.},
 issue_date = {August 2004},
 volume = {23},
 number = {3},
 month = aug,
 year = {2004},
 issn = {0730-0301},
 pages = {309--314},
 numpages = {6},
 
 doi = {10.1145/1015706.1015720},
 acmid = {1015720},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {Alpha Matting, Foreground extraction, Graph Cuts, Image Editing, Interactive Image Segmentation},
} 
%  url = {http://doi.acm.org/10.1145/1015706.1015720},
[download]
@inproceedings{Rother:2004:GIF:1186562.1015720,
 author = {Rother, Carsten and Kolmogorov, Vladimir and Blake, Andrew},
 title = {"GrabCut": Interactive Foreground Extraction Using Iterated Graph Cuts},
 booktitle = {ACM SIGGRAPH 2004 Papers},
 series = {SIGGRAPH '04},
 year = {2004},
 location = {Los Angeles, California},
 pages = {309--314},
 numpages = {6},
 
 doi = {10.1145/1186562.1015720},
 acmid = {1015720},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {Alpha Matting, Foreground extraction, Graph Cuts, Image Editing, Interactive Image Segmentation},
} 
%  url = {http://doi.acm.org/10.1145/1186562.1015720},

@inproceedings{Hou2016,
  title={Bottom-Up Top-Down Cues for Weakly-Supervised Semantic Segmentation},
  author={Qibin Hou and Daniela Massiceti and Puneet Kumar Dokania and Yunchao Wei and Ming-Ming Cheng and Philip H. S. Torr},
  booktitle={EMMCVPR},
  year={2016}
}




@ARTICLE{STC2017,
author={Y. {Wei} and X. {Liang} and Y. {Chen} and X. {Shen} and M. {Cheng} and J. {Feng} and Y. {Zhao} and S. {Yan}},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
title={STC: A Simple to Complex Framework for Weakly-Supervised Semantic Segmentation},
year={2017},
volume={39},
number={11},
pages={2314-2320},
keywords={image classification;image representation;image segmentation;learning (artificial intelligence);neural nets;object detection;weakly-supervised semantic segmentation;semantic object segmentation;deep convolutional neural networks;pixel-level segmentation masks;image-level annotations;initial segmentation network;saliency maps;simple images;supervision information;Enhanced-DCNN;predicted segmentation masks;complex images;PASCAL VOC 2012 segmentation benchmark;STC framework;DCNN;simple to complex framework;bottom-up salient object detection techniques;Image segmentation;Semantics;Training;Electronic mail;Object detection;Neural networks;Benchmark testing;Semantic segmentation;weakly-supervised learning;convolutional neural network},
doi={10.1109/TPAMI.2016.2636150},
ISSN={},
month={Nov},}

% ////////////::::::::::Fully SUpervised training references:::::::::::://///////////
@InProceedings{Arif2018,
author="Al Arif and S. M. Masudur Rahman
and Knapp, Karen
and Slabaugh, Greg",
title="Shape-Aware Deep Convolutional Neural Network for Vertebrae Segmentation",
booktitle="Computational Methods and Clinical Applications in Musculoskeletal Imaging",
year="2018",
pages="12--24",
abstract="Shape is an important characteristic of an object, and a fundamental topic in computer vision. In image segmentation, shape has been widely used in segmentation methods, like the active shape model, to constrain a segmentation result to a class of learned shapes. However, to date, shape has been underutilized in deep segmentation networks. This paper addresses this gap by introducing a shape-aware term in the segmentation loss function. A deep convolutional network has been adapted in a novel cervical vertebrae segmentation framework and compared with traditional active shape model-based methods. The proposed framework has been trained on an augmented dataset of 26370 vertebrae and tested on 792 vertebrae collected from a total of 296 real-life emergency room lateral cervical X-ray images. The proposed framework achieved an average error of 1.11 pixels, signifying a 36{\%} improvement over the traditional methods. The introduction of the novel shape-aware term in the loss function significantly improved the performance by further 12{\%}, achieving an average error of only 0.99 pixel.",
isbn="978-3-319-74113-0"
}



@InProceedings{Weese2001,
author="Weese, J{\"u}rgen
and Kaus, Michael
and Lorenz, Christian
and Lobregt, Steven
and Truyen, Roel
and Pekar, Vladimir",
editor="Insana, Michael F.
and Leahy, Richard M.",
title="Shape Constrained Deformable Models for 3D Medical Image Segmentation",
booktitle="Information Processing in Medical Imaging",
year="2001",
pages="380--387",
abstract="To improve the robustness of segmentation methods, more and more methods use prior knowledge. We present an approach which embeds an active shape model into an elastically deformable surface model, and combines the advantages of both approaches. The shape model constrains the flexibility of the surface mesh representing the deformable model and maintains an optimal distribution of mesh vertices. A specific external energy which attracts the deformable model to locally detected surfaces, reduces the danger that the mesh is trapped by false object boundaries. Examples are shown, and furthermore a validation study for the segmentation of vertebrae in CT images is presented. With the exception of a few problematic areas, the algorithm leads reliably to a very good overall segmentation.",
isbn="978-3-540-45729-9"
}


@incollection{BenTaieb2016,
	title = {Topology {Aware} {Fully} {Convolutional} {Networks} for {Histology} {Gland} {Segmentation}},
	volume = {9901},
	isbn = {978-3-319-46722-1 978-3-319-46723-8},
	url = {http://link.springer.com/10.1007/978-3-319-46723-8_53},
	abstract = {The recent success of deep learning techniques in classiﬁcation and object detection tasks has been leveraged for segmentation tasks. However, a weakness of these deep segmentation models is their limited ability to encode high level shape priors, such as smoothness and preservation of complex interactions between object regions, which can result in implausible segmentations. In this work, by formulating and optimizing a new loss, we introduce the ﬁrst deep network trained to encode geometric and topological priors of containment and detachment. Our results on the segmentation of histology glands from a dataset of 165 images demonstrate the advantage of our novel loss terms and show how our topology aware architecture outperforms competing methods by up to 10\% in both pixel-level accuracy and object-level Dice.},
	language = {en},
	urldate = {2019-10-28},
	booktitle = {MICCAI},
	author = {BenTaieb, Aïcha and Hamarneh, Ghassan},
	year = {2016},
	doi = {10.1007/978-3-319-46723-8_53},
	pages = {460--468}
}


@inproceedings{Chieh2015,
  TITLE = {{Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs}},
  AUTHOR = {Liang-Chieh, Chen and Papandreou, George and Kokkinos, Iasonas and murphy, kevin and Yuille, Alan},
  
  BOOKTITLE = {{International Conference on Learning Representations}},
  ADDRESS = {San Diego, United States},
  YEAR = {2015},
  MONTH = May,
  HAL_ID = {hal-01263610},
  HAL_VERSION = {v1},
}
%  URL = {https://hal.inria.fr/hal-01263610},
@article{ParseNet2015,
  author    = {Wei Liu and
               Andrew Rabinovich and
               Alexander C. Berg},
  title     = {},
  journal   = {CoRR},
  volume    = {abs/1506.04579},
  year      = {2015},
  archivePrefix = {arXiv},
  eprint    = {1506.04579},
  timestamp = {Mon, 13 Aug 2018 16:48:41 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/LiuRB15},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@InProceedings{Yan2018,
author="Yan, Zengqiang
and Yang, Xin
and Cheng, Kwang-Ting Tim",
title="A Deep Model with Shape-Preserving Loss for Gland Instance Segmentation",
booktitle="MICCAI",
year="2018",
pages="138--146",
abstract="Segmenting gland instance in histology images requires not only separating glands from a complex background but also identifying each gland individually via accurate boundary detection. This is a very challenging task due to lots of noises from the background, tiny gaps between adjacent glands, and the ``coalescence'' problem arising from adhesive gland instances. State-of-the-art methods adopted multi-channel/multi-task deep models to separately accomplish pixel-wise gland segmentation and boundary detection, yielding a high model complexity and difficulties in training. In this paper, we present a unified deep model with a new shape-preserving loss which facilities the training for both pixel-wise gland segmentation and boundary detection simultaneously. The proposed shape-preserving loss helps significantly reduce the model complexity and make the training process more controllable. Compared with the current state-of-the-art methods, the proposed deep model with the shape-preserving loss achieves the best overall performance on the 2015 MICCAI Gland Challenge dataset. In addition, the flexibility of integrating the proposed shape-preserving loss into any learning based medical image segmentation networks offers great potential for further performance improvement of other applications.",
isbn="978-3-030-00934-2"
}
@inproceedings{Zotti2017,
  author    = {Cl{\'{e}}ment Zotti and
               Zhiming Luo and
               Olivier Humbert and
               Alain Lalande and
               Pierre{-}Marc Jodoin},
  title     = {GridNet with Automatic Shape Prior Registration for Automatic {MRI}
               Cardiac Segmentation},
  booktitle = {Statistical Atlases and Computational Models of the Heart {STACOM},
               Held in Conjunction with {MICCAI}, Quebec City, Canada},
  series    = {LNCS},
  volume    = {10663},
  pages     = {73--81},
  year      = {2017},
  doi       = {10.1007/978-3-319-75541-0\_8},
  timestamp = {Tue, 14 May 2019 10:00:50 +0200},
  biburl    = {https://dblp.org/rec/bib/conf/miccai/ZottiLHLJ17},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
%    url       = {https://doi.org/10.1007/978-3-319-75541-0\_8},

% ::::::::::::::::::::::::::::::::Non medical applications:::
@article{Vladimir2018,
  author    = {Vladimir Iglovikov and
               Alexey Shvets},
  title     = {TernausNet: U-Net with {VGG11} Encoder Pre-Trained on ImageNet for
               Image Segmentation},
  journal   = {CoRR},
  volume    = {abs/1801.05746},
  year      = {2018},
  archivePrefix = {arXiv},
  eprint    = {1801.05746},
  timestamp = {Mon, 13 Aug 2018 16:48:59 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1801-05746},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{Jia2018,
  author    = {Jia Guo and
               Jiankang Deng and
               Niannan Xue and
               Stefanos Zafeiriou},
  title     = {Stacked Dense U-Nets with Dual Transformers for Robust Face Alignment},
  journal   = {CoRR},
  volume    = {abs/1812.01936},
  year      = {2018},
  archivePrefix = {arXiv},
  eprint    = {1812.01936},
  timestamp = {Tue, 01 Jan 2019 15:01:25 +0100},
  biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1812-01936},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

% url       = {http://arxiv.org/abs/1806.03185},
  archivePrefix = {arXiv},
@article{Tang2018 ,
  author    = {Zhiqiang Tang and
               Xi Peng and
               Shijie Geng and
               Yizhe Zhu and
               Dimitris N. Metaxas},
  title     = {CU-Net: Coupled U-Nets},
  journal   = {CoRR},
  volume    = {abs/1808.06521},
  year      = {2018},
 
  archivePrefix = {arXiv},
  eprint    = {1808.06521},
  timestamp = {Sun, 02 Sep 2018 15:01:55 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1808-06521},
  bibsource = {dblp computer science bibliography, https://dblp.org}

}


@article{Stoller2018,
  author    = {Daniel Stoller and
               Sebastian Ewert and
               Simon Dixon},
  title     = {Wave-U-Net: {A} Multi-Scale Neural Network for End-to-End Audio Source
               Separation},
  journal   = {CoRR},
  volume    = {abs/1806.03185},
  year      = {2018},
  
  eprint    = {1806.03185},
  timestamp = {Mon, 13 Aug 2018 16:47:13 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1806-03185},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{
ma2020how,
title={How Distance Transform Maps Boost Segmentation {\{}CNN{\}}s: An Empirical Study},
author={Jun Ma and Zhan Wei and Yiwen Zhang and Yixin Wang and Rongfei Lv and Cheng Zhu and Gaoxiang Chen and Jianan Liu and Chao Peng and Lei Wang and Yunpeng Wang and Jianan Chen},
booktitle={Medical Imaging with Deep Learning},
year={2020},
url={https://openreview.net/forum?id=gsKS0baY1B}
}

@INPROCEEDINGS{Long2015,
author={J. {Long} and E. {Shelhamer} and T. {Darrell}},
booktitle={CVPR},
title={Fully convolutional networks for semantic segmentation},
year={2015},
volume={},
number={},
pages={3431-3440},
abstract={Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, exceed the state-of-the-art in semantic segmentation. Our key insight is to build “fully convolutional” networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning. We define and detail the space of fully convolutional networks, explain their application to spatially dense prediction tasks, and draw connections to prior models. We adapt contemporary classification networks (AlexNet [20], the VGG net [31], and GoogLeNet [32]) into fully convolutional networks and transfer their learned representations by fine-tuning [3] to the segmentation task. We then define a skip architecture that combines semantic information from a deep, coarse layer with appearance information from a shallow, fine layer to produce accurate and detailed segmentations. Our fully convolutional network achieves state-of-the-art segmentation of PASCAL VOC (20% relative improvement to 62.2% mean IU on 2012), NYUDv2, and SIFT Flow, while inference takes less than one fifth of a second for a typical image.},
keywords={image classification;image segmentation;inference mechanisms;learning (artificial intelligence);fully convolutional networks;semantic segmentation;visual models;pixels-to-pixels;inference;learning;contemporary classification networks;PASCAL VOC;NYUDv2;SIFT flow;Semantics;Training;Convolution;Image segmentation;Computer architecture;Deconvolution;Adaptation models},
doi={10.1109/CVPR.2015.7298965},
ISSN={},
month={June},}

@incollection{Jifeng2016,
title = {R-FCN: Object Detection via Region-based Fully Convolutional Networks},
author = {Dai, Jifeng and Li, Yi and He, Kaiming and Sun, Jian},
booktitle = {Advances in Neural Information Processing Systems 29},
editor = {D. D. Lee and M. Sugiyama and U. V. Luxburg and I. Guyon and R. Garnett},
pages = {379--387},
year = {2016},
}
% url = {http://papers.nips.cc/paper/6465-r-fcn-object-detection-via-region-based-fully-convolutional-networks.pdf}


@INPROCEEDINGS{Laina2016,
author={I. {Laina} and C. {Rupprecht} and V. {Belagiannis} and F. {Tombari} and N. {Navab}},
booktitle={2016 Fourth International Conference on 3D Vision (3DV)},
title={Deeper Depth Prediction with Fully Convolutional Residual Networks},
year={2016},
volume={},
number={},
pages={239-248},
abstract={This paper addresses the problem of estimating the depth map of a scene given a single RGB image. We propose a fully convolutional architecture, encompassing residual learning, to model the ambiguous mapping between monocular images and depth maps. In order to improve the output resolution, we present a novel way to efficiently learn feature map up-sampling within the network. For optimization, we introduce the reverse Huber loss that is particularly suited for the task at hand and driven by the value distributions commonly present in depth maps. Our model is composed of a single architecture that is trained end-to-end and does not rely on post-processing techniques, such as CRFs or other additional refinement steps. As a result, it runs in real-time on images or videos. In the evaluation, we show that the proposed model contains fewer parameters and requires fewer training data than the current state of the art, while outperforming all approaches on depth estimation. Code and models are publicly available.},
keywords={convolution;estimation theory;image colour analysis;image resolution;learning (artificial intelligence);neural nets;optimisation;statistical distributions;depth prediction;fully convolutional residual network;depth map estimation;RGB image;output resolution;feature map learning;optimization;reverse Huber loss;value distribution;Estimation;Image resolution;Three-dimensional displays;Computer architecture;Computer vision;Semantics;Optimization;Depth prediction;CNN},
doi={10.1109/3DV.2016.32},
ISSN={},
month={Oct},}

@article{boykov2006graph,
  title={Graph cuts and efficient ND image segmentation},
  author={Boykov, Yuri and Funka-Lea, Gareth},
  journal={International journal of computer vision},
  volume={70},
  number={2},
  pages={109--131},
  year={2006},
  publisher={Springer}
}

@incollection{xu2000image,
  title={Image segmentation using deformable models},
  author={Xu, Chenyang and Pham, Dzung L and Prince, Jerry L},
  booktitle={Handbook of medical imaging},
  volume={2},
  number={3},
  pages={129--174},
  year={2000},
  publisher={SPIE}
}

@article{iglesias2015multi,
  title={Multi-atlas segmentation of biomedical images: a survey},
  author={Iglesias, Juan Eugenio and Sabuncu, Mert R},
  journal={Medical image analysis},
  volume={24},
  number={1},
  pages={205--219},
  year={2015},
  publisher={Elsevier}
}

@article{cootes1994use,
  title={Use of active shape models for locating structures in medical images},
  author={Cootes, Timothy F and Hill, Andrew and Taylor, Christopher J and Haslam, Jane},
  journal={Image and vision computing},
  volume={12},
  number={6},
  pages={355--365},
  year={1994},
  publisher={Elsevier}
}

@article{shi2000normalized,
  title={Normalized cuts and image segmentation},
  author={Shi, Jianbo and Malik, Jitendra},
  journal={IEEE Transactions on pattern analysis and machine intelligence},
  volume={22},
  number={8},
  pages={888--905},
  year={2000},
  publisher={Ieee}
}

@InProceedings{Gibson2017,
author="Gibson, Eli
and Giganti, Francesco
and Hu, Yipeng
and Bonmati, Ester
and Bandula, Steve
and Gurusamy, Kurinchi
and Davidson, Brian R.
and Pereira, Stephen P.
and Clarkson, Matthew J.
and Barratt, Dean C.",
editor="Descoteaux, Maxime
and Maier-Hein, Lena
and Franz, Alfred
and Jannin, Pierre
and Collins, D. Louis
and Duchesne, Simon",
title="Towards Image-Guided Pancreas and Biliary Endoscopy: Automatic Multi-organ Segmentation on Abdominal CT with Dense Dilated Networks",
booktitle="Medical Image Computing and Computer Assisted Intervention - MICCAI",
year="2017",
pages="728--736",
abstract="Segmentation of anatomy on abdominal CT enables patient-specific image guidance in clinical endoscopic procedures and in endoscopy training. Because robust interpatient registration of abdominal images is necessary for existing multi-atlas- and statistical-shape-model-based segmentations, but remains challenging, there is a need for automated multi-organ segmentation that does not rely on registration. We present a deep-learning-based algorithm for segmenting the liver, pancreas, stomach, and esophagus using dilated convolution units with dense skip connections and a new spatial prior. The algorithm was evaluated with an 8-fold cross-validation and compared to a joint-label-fusion-based segmentation based on Dice scores and boundary distances. The proposed algorithm yielded more accurate segmentations than the joint-label-fusion-based algorithm for the pancreas (median Dice scores 66 vs 37), stomach (83 vs 72) and esophagus (73 vs 54) and marginally less accurate segmentation for the liver (92 vs 93). We conclude that dilated convolutional networks with dense skip connections can segment the liver, pancreas, stomach and esophagus from abdominal CT without image registration and have the potential to support image-guided navigation in gastrointestinal endoscopy procedures.",
isbn="978-3-319-66182-7"
}


@InProceedings{Zhou2016,
author="Zhou, Xiangrong
and Ito, Takaaki
and Takayama, Ryosuke
and Wang, Song
and Hara, Takeshi
and Fujita, Hiroshi",
title="Three-Dimensional CT Image Segmentation by Combining 2D Fully Convolutional Network with 3D Majority Voting",
booktitle="Deep Learning and Data Labeling for Medical Applications",
year="2016",
pages="111--120",
abstract="We propose a novel approach for automatic segmentation of anatomical structures on 3D CT images by voting from a fully convolutional network (FCN), which accomplishes an end-to-end, voxel-wise multiple-class classification to map each voxel in a CT image directly to an anatomical label. The proposed method simplifies the segmentation of the anatomical structures (including multiple organs) in a CT image (generally in 3D) to majority voting for the semantic segmentation of multiple 2D slices drawn from different viewpoints with redundancy. An FCN consisting of ``convolution'' and ``de-convolution'' parts is trained and re-used for the 2D semantic image segmentation of different slices of CT scans. All of the procedures are integrated into a simple and compact all-in-one network, which can segment complicated structures on differently sized CT images that cover arbitrary CT scan regions without any adjustment. We applied the proposed method to segment a wide range of anatomical structures that consisted of 19 types of targets in the human torso, including all the major organs. A database consisting of 240 3D CT scans and a humanly annotated ground truth was used for training and testing. The results showed that the target regions for the entire set of CT test scans were segmented with acceptable accuracies (89 {\%} of total voxels were labeled correctly) against the human annotations. The experimental results showed better efficiency, generality, and flexibility of this end-to-end learning approach on CT image segmentations comparing to conventional methods guided by human expertise.",
isbn="978-3-319-46976-8"
}


@INPROCEEDINGS{Li2018_UIS,
author={X. {Li} and S. {Wang} and X. {Wei} and J. {Zhu} and R. {Yu} and M. {Zhao} and M. {Yu} and Z. {Liu} and S. {Liu}},
booktitle={IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS)},
title={Fully Convolutional Networks for Ultrasound Image Segmentation of Thyroid Nodules},
year={2018},
volume={},
number={},
pages={886-890},
abstract={Ultrasound image segmentation plays an important role in judgement of benign and malignant thyroid nodules. Compared with the traditional convolutional neural network, the fully convolutional networks has better sparsity, higher precision and faster training speed. In this paper, we develop an 8-layer fully convolutional networks for ultrasound image segmentation of thyroid nodules, which is called FCN-Thyroid Nodules, or FCN-TN for short. We constructed a data set with 300 images to train FCN-TN. Each nodule is delineated by expert and served as ground truth for making comparison. The segmentation accuracy of 91% is obtained on the proposed network with 100 test images, which indicates that the fully convolutional networks has great potential in the field of ultrasound image segmentation of thyroid nodules.},
keywords={biomedical ultrasonics;fuzzy neural nets;image classification;image segmentation;learning (artificial intelligence);medical image processing;neural nets;test images;FCN-Thyroid Nodules;8-layer fully convolutional networks;traditional convolutional neural network;malignant thyroid nodules;benign thyroid nodules;segmentation accuracy;FCN-TN;Image segmentation;Ultrasonic imaging;Cancer;Convolution;Biomedical imaging;Training;Neural networks;Thyroid nodule;Ultrasound image processing;Convolutional neural network;Segmentation},
doi={10.1109/HPCC/SmartCity/DSS.2018.00147},
ISSN={},
month={June},}



@misc{Medical_Torch,
  author       = {Christian S. Perone and
                  cclauss and
                  Elvis Saravia and
                  Pedro Lemos Ballester and
                  MohitTare},
  title        = {perone/medicaltorch: Release v0.2},
  month        = nov,
  year         = 2018,
  doi          = {10.5281/zenodo.1495335},
 
}
%  url          = {https://doi.org/10.5281/zenodo.1495335}
%///////////////////////////////////:::
@ARTICLE{Samek18,
  author = {Gr{\'e}goire Montavon and Wojciech Samek and Klaus-Robert M{\"u}ller},
  title = {Methods for Interpreting and Understanding Deep Neural Networks},
  journal = {Digital Signal Processing},
  year = {2018},
  volume = {73},
  pages = {1-15},
  doi = {10.1016/j.dsp.2017.10.011},
  
}
% url = {https://doi.org/10.1016/j.dsp.2017.10.011}
@Inbook{Samek2019a,
author="Samek, Wojciech
and M{\"u}ller, Klaus-Robert",
editor="Samek, Wojciech
and Montavon, Gr{\'e}goire
and Vedaldi, Andrea
and Hansen, Lars Kai
and Muller, Klaus-Robert",
title="Towards Explainable Artificial Intelligence",
bookTitle="Explainable AI: Interpreting, Explaining and Visualizing Deep Learning",
year="2019",
publisher="Springer International Publishing",
address="Cham",
pages="5--22",
abstract="In recent years, machine learning (ML) has become a key enabling technology for the sciences and industry. Especially through improvements in methodology, the availability of large databases and increased computational power, today's ML algorithms are able to achieve excellent performance (at times even exceeding the human level) on an increasing number of complex tasks. Deep learning models are at the forefront of this development. However, due to their nested non-linear structure, these powerful models have been generally considered ``black boxes'',Black-Box AI not providing any information about what exactly makes them arrive at their predictions. Since in many applications, e.g., in the medical domain, such lack of transparency may be not acceptable, the development of methods for visualizing, explaining and interpreting deep learning models has recently attracted increasing attention. This introductory paper presents recent developments and applications in this field and makes a plea for a wider use of explainable learning algorithms in practice.",
isbn="978-3-030-28954-6",
doi="10.1007/978-3-030-28954-6"
}
@inbook{Samek2019b,
author="Samek, Wojciech
and M{\"u}ller, Klaus-Robert",
editor="Samek, Wojciech
and Montavon, Gr{\'e}goire
and Vedaldi, Andrea
and Hansen, Lars Kai
and Muller, Klaus-Robert",
title="Towards Explainable Artificial Intelligence",
booktitle="Explainable AI: Interpreting, Explaining and Visualizing Deep Learning",
year="2019",
publisher="Springer International Publishing",
address="Cham",
pages="5--22",
isbn="978-3-030-28954-6",
doi="10.1007/978-3-030-28954-6_1"
}

@inbook{Montavon2019,
author="Montavon, Gr{\'e}goire
and Binder, Alexander
and Lapuschkin, Sebastian
and Samek, Wojciech
and M{\"u}ller, Klaus-Robert",
editor="Samek, Wojciech
and Montavon, Gr{\'e}goire
and Vedaldi, Andrea
and Hansen, Lars Kai
and Muller, Klaus-Robert",
title="Layer-Wise Relevance Propagation: An Overview",
booktitle="Explainable AI: Interpreting, Explaining and Visualizing Deep Learning",
year="2019",
publisher="Springer International Publishing",
address="Cham",
pages="193--209",
isbn="978-3-030-28954-6",
doi="10.1007/978-3-030-28954-6_10",
}


@ARTICLE{bach15,
    author = {Bach, Sebastian AND Binder, Alexander AND Montavon, Gr{\'e}goire AND Klauschen, Frederick AND M{\"u}ller, Klaus-Robert AND Samek, Wojciech},
    journal = {PLoS ONE},
    publisher = {Public Library of Science},
    title = {On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation},
    year = {2015},
    month = {07},
    volume = {10},
    
    pages = {e0130140},
    number = {7},
    doi = {10.1371/journal.pone.0130140}
}
%  url = {http://dx.doi.org/10.1371%2Fjournal.pone.0130140},
@article{TahaH15,
  abstract = {Background
Medical Image segmentation is an important image processing step. Comparing images to evaluate the quality of segmentation is an essential part of measuring progress in this research area. Some of the challenges in evaluating medical segmentation are: metric selection, the use in the literature of multiple definitions for certain metrics, inefficiency of the metric calculation implementations leading to difficulties with large volumes, and lack of support for fuzzy segmentation by existing metrics.

Result
First we present an overview of 20 evaluation metrics selected based on a comprehensive literature review. For fuzzy segmentation, which shows the level of membership of each voxel to multiple classes, fuzzy definitions of all metrics are provided. We present a discussion about metric properties to provide a guide for selecting evaluation metrics. Finally, we propose an efficient evaluation tool implementing the 20 selected metrics. The tool is optimized to perform efficiently in terms of speed and required memory, also if the image size is extremely large as in the case of whole body MRI or CT volume segmentation. An implementation of this tool is available as an open source project.

Conclusion
We propose an efficient evaluation tool for 3D medical image segmentation using 20 evaluation metrics and provide guidelines for selecting a subset of these metrics that is suitable for the data and the segmentation task.},
  added-at = {2015-10-17T23:01:32.000+0200},
  author = {Taha, Abdel Aziz and Hanbury, Allan},
  
  interhash = {5e1a2169d97771c1eb1bac43245f7984},
  intrahash = {5de4c45dde21271f11bc0b8ed942df08},
  journal = {BMC Medical Imaging},
  keywords = {ataha hanbury},
  month = aug,
  pages = 29,
  timestamp = {2015-10-17T23:08:45.000+0200},
  title = {Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool},
  volume = 15,
  year = 2015
}
%  biburl = {https://www.bibsonomy.org/bibtex/25de4c45dde21271f11bc0b8ed942df08/ataha},

@article{Ganaye2019,
  year = {2019},
  month = dec,
  publisher = {Elsevier {BV}},
  volume = {58},
  pages = {101551},
  author = {Pierre-Antoine Ganaye and Michaël Sdika and Bill Triggs and Hugues Benoit-Cattin},
  title = {Removing segmentation inconsistencies with semi-supervised non-adjacency constraint},
  journal = {Med Image Anal}
}

@article{Lambert2019,
  title={SegTHOR: Segmentation of Thoracic Organs at Risk in CT images},
  author={Zoltan Lambert and Caroline Petitjean and Bernard Dubray and Shasha Ruan},
  journal={ArXiv},
  year={2019},
  volume={abs/1912.05950}
}

@InProceedings{UOLO2018,
author="Ara{\'u}jo, Teresa
and Aresta, Guilherme
and Galdran, Adrian
and Costa, Pedro
and Mendon{\c{c}}a, Ana Maria
and Campilho, Aur{\'e}lio",
title="UOLO - Automatic Object Detection and Segmentation in Biomedical Images",
booktitle="Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support",
year="2018",
publisher="Springer International Publishing",
address="Cham",
pages="165--173",
abstract="We propose UOLO, a novel framework for the simultaneous detection and segmentation of structures of interest in medical images. UOLO consists of an object segmentation module which intermediate abstract representations are processed and used as input for object detection. The resulting system is optimized simultaneously for detecting a class of objects and segmenting an optionally different class of structures. UOLO is trained on a set of bounding boxes enclosing the objects to detect, as well as pixel-wise segmentation information, when available. A new loss function is devised, taking into account whether a reference segmentation is accessible for each training image, in order to suitably backpropagate the error. We validate UOLO on the task of simultaneous optic disc (OD) detection, fovea detection, and OD segmentation from retinal images, achieving state-of-the-art performance on public datasets.",
isbn="978-3-030-00889-5"
}


@InProceedings{Toussaint2018,
author="Toussaint, Nicolas
and Khanal, Bishesh
and Sinclair, Matthew
and Gomez, Alberto
and Skelton, Emily
and Matthew, Jacqueline
and Schnabel, Julia A.",
title="Weakly Supervised Localisation for Fetal Ultrasound Images",
booktitle="Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support",
year="2018",
publisher="Springer International Publishing",
address="Cham",
pages="192--200",
abstract="This paper addresses the task of detecting and localising fetal anatomical regions in 2D ultrasound images, where only image-level labels are present at training, i.e. without any localisation or segmentation information. We examine the use of convolutional neural network architectures coupled with soft proposal layers. The resulting network simultaneously performs anatomical region detection (classification) and localisation tasks. We generate a proposal map describing the attention of the network for a particular class. The network is trained on 85,500 2D fetal Ultrasound images and their associated labels. Labels correspond to six anatomical regions: head, spine, thorax, abdomen, limbs, and placenta. Detection achieves an average accuracy of 90{\%} on individual regions, and show that the proposal maps correlate well with relevant anatomical structures. This work presents itself as a powerful and essential step towards subsequent tasks such as fetal position and pose estimation, organ-specific segmentation, or image-guided navigation.",
isbn="978-3-030-00889-5"
}

@InProceedings{gcnn2016,
author = {Najibi, Mahyar and Rastegari, Mohammad and Davis, Larry S.},
title = {G-CNN: An Iterative Grid Based Object Detector},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2016}
}

@InProceedings{Liu2016,
author="Liu, Wei
and Anguelov, Dragomir
and Erhan, Dumitru
and Szegedy, Christian
and Reed, Scott
and Fu, Cheng-Yang
and Berg, Alexander C.",
editor="Leibe, Bastian
and Matas, Jiri
and Sebe, Nicu
and Welling, Max",
title="SSD: Single Shot MultiBox Detector",
booktitle="Computer Vision -- ECCV 2016",
year="2016",
publisher="Springer International Publishing",
address="Cham",
pages="21--37",
abstract="We present a method for detecting objects in images using a single deep neural network. Our approach, named SSD, discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location. At prediction time, the network generates scores for the presence of each object category in each default box and produces adjustments to the box to better match the object shape. Additionally, the network combines predictions from multiple feature maps with different resolutions to naturally handle objects of various sizes. SSD is simple relative to methods that require object proposals because it completely eliminates proposal generation and subsequent pixel or feature resampling stages and encapsulates all computation in a single network. This makes SSD easy to train and straightforward to integrate into systems that require a detection component. Experimental results on the PASCAL VOC, COCO, and ILSVRC datasets confirm that SSD has competitive accuracy to methods that utilize an additional object proposal step and is much faster, while providing a unified framework for both training and inference. For {\$}{\$}300 {\backslash}times 300{\$}{\$}300{\texttimes}300input, SSD achieves 74.3 {\%} mAP on VOC2007 test at 59 FPS on a Nvidia Titan X and for {\$}{\$}512 {\backslash}times 512{\$}{\$}512{\texttimes}512input, SSD achieves 76.9 {\%} mAP, outperforming a comparable state of the art Faster R-CNN model. Compared to other single stage methods, SSD has much better accuracy even with a smaller input image size. Code is available at https://github.com/weiliu89/caffe/tree/ssd.",
isbn="978-3-319-46448-0"
}


@INPROCEEDINGS{Lu2011, 

author={L. {Lu} and J. {Bi} and M. {Wolf} and M. {Salganicoff}}, booktitle={CVPR 2011}, title={Effective 3D object detection and regression using probabilistic segmentation features in CT images}, year={2011}, volume={}, number={}, pages={1049-1056},}

@ARTICLE{Jiao2019,

  author={L. {Jiao} and F. {Zhang} and F. {Liu} and S. {Yang} and L. {Li} and Z. {Feng} and R. {Qu}},
  journal={IEEE Access}, 
 title={A Survey of Deep Learning-Based Object Detection}, 
  year={2019},
  volume={7},
  number={},
  pages={128837-128868},
  abstract={Object detection is one of the most important and challenging branches of computer vision, which has been widely applied in people's life, such as monitoring security, autonomous driving and so on, with the purpose of locating instances of semantic objects of a certain class. With the rapid development of deep learning algorithms for detection tasks, the performance of object detectors has been greatly improved. In order to understand the main development status of object detection pipeline thoroughly and deeply, in this survey, we analyze the methods of existing typical detection models and describe the benchmark datasets at first. Afterwards and primarily, we provide a comprehensive overview of a variety of object detection methods in a systematic manner, covering the one-stage and two-stage detectors. Moreover, we list the traditional and new applications. Some representative branches of object detection are analyzed as well. Finally, we discuss the architecture of exploiting these object detection methods to build an effective and efficient system and point out a set of development trends to better follow the state-of-the-art algorithms and further research.},
  keywords={computer vision;learning (artificial intelligence);object detection;object detection methods;semantic objects;deep learning algorithms;computer vision;Licenses;Classification;deep learning;localization;object detection;typical pipelines},
  doi={10.1109/ACCESS.2019.2939201},
  ISSN={2169-3536},
  month={},}


@INPROCEEDINGS{Lu2019,

  author={W. {Lu} and Y. {Zhou} and G. {Wan} and S. {Hou} and S. {Song}},
  booktitle={CVPR}, 
 title={L3-Net: Towards Learning Based LiDAR Localization for Autonomous Driving}, 
  year={2019},
  volume={},
  number={},
  pages={6382-6391},
  abstract={We present L3-Net - a novel learning-based LiDAR localization system that achieves centimeter-level localization accuracy, comparable to prior state-of-the-art systems with hand-crafted pipelines. Rather than relying on these hand-crafted modules, we innovatively implement the use of various deep neural network structures to establish a learning-based approach. L3-Net learns local descriptors specifically optimized for matching in different real-world driving scenarios. 3D convolutions over a cost volume built in the solution space significantly boosts the localization accuracy. RNNs are demonstrated to be effective in modeling the vehicle's dynamics, yielding better temporal smoothness and accuracy. We comprehensively validate the effectiveness of our approach using freshly collected datasets. Multiple trials of repetitive data collection over the same road and areas make our dataset ideal for testing localization systems. The SunnyvaleBigLoop sequences, with a year's time interval between the collected mapping and testing data, made it quite challenging, but the low localization error of our method in these datasets demonstrates its maturity for real industrial implementation.},
  keywords={convolution;learning (artificial intelligence);mobile robots;optical radar;recurrent neural nets;SLAM (robots);stereo image processing;traffic engineering computing;L3-Net;autonomous driving;centimeter-level localization accuracy;deep neural network structures;learning-based approach;learning-based LiDAR localization system;3D convolutions;RNNs;SunnyvaleBigLoop sequences;Robotics + Driving;Vision Applications and Systems},
  doi={10.1109/CVPR.2019.00655},
  ISSN={2575-7075},
  month={June},}


@techreport{MISDecathlon,  title       = "A large annotated medical image dataset for the development and evaluation of segmentation algorithms",  abstract    = "Semantic segmentation of medical images aims to associate a pixel with a label in a medical image without human initialization. The success of semantic segmentation algorithms is contingent on the availability of high-quality imaging data with corresponding labels provided by experts. We sought to create a large collection of annotated medical image datasets of various clinically relevant anatomies available under open source license to facilitate the development of semantic segmentation algorithms. Such a resource would allow: 1) objective assessment of general-purpose segmentation methods through comprehensive benchmarking and 2) open and free access to medical image data for any researcher interested in the problem domain. Through a multi-institutional effort, we generated a large, curated dataset representative of several highly variable segmentation tasks that was used in a crowd-sourced challenge - the Medical Segmentation Decathlon held during the 2018 Medical Image Computing and Computer Aided Interventions Conference in Granada, Spain. Here, we describe these ten labeled image datasets so that these data may be effectively reused by the research community.",  keywords    = "cs.CV, eess.IV",  author      = "Simpson, {Amber L.} and Michela Antonelli and Spyridon Bakas and Michel Bilello and Keyvan Farahani and Ginneken, {Bram van} and Annette Kopp-Schneider and Landman, {Bennett A.} and Geert Litjens and Bjoern Menze and Olaf Ronneberger and Summers, {Ronald M.} and Patrick Bilic and Christ, {Patrick F.} and Do, {Richard K. G.} and Marc Gollub and Jennifer Golia-Pernicka and Heckers, {Stephan H.} and Jarnagin, {William R.} and McHugo, {Maureen K.} and Sandy Napel and Eugene Vorontsov and Lena Maier-Hein and Cardoso, {M. Jorge}",  year        = "2019",  month       = "2",  day         = "25",  language    = "English",  series      = "arXiv",  type        = "WorkingPaper", }


@inproceedings{Hauss1998,
  title={On the Hausdorff Distance Used for the Evaluation of Segmentation Results},
  author={Mario Beauchemin and Keith P. B. Thomson and Geoffrey Edwards},
  year={1998}
}



@inproceedings{Tofighi2018,
  added-at = {},
  author = {{Tofighi}, M. and {Guo}, T. and {Vanamala}, J. K. P. and {Monga}, V.},
  biburl = {https://www.bibsonomy.org/bibtex/25a239b64a7a0381dc2c4acca2af53cde/dickscheid},
  booktitle = {2018 25th IEEE International Conference on Image Processing (ICIP)},
  doi = {10.1109/ICIP.2018.8451797},
  interhash = {d0637e3ad930cd6d95891d3aef9f6a63},
  intrahash = {5a239b64a7a0381dc2c4acca2af53cde},
  issn = {2381-8549},
  keywords = {cell_segmentation image_segmentation read shape_priors},
  month = oct,
  pages = {719-723},
  timestamp = {2019-05-03T12:38:34.000+0200},
  title = {Deep Networks with Shape Priors for Nucleus Detection},
  year = 2018
}





@article{tofighi_deep_2018,
	title = {Deep {Networks} with {Shape} {Priors} for {Nucleus} {Detection}},
	url = {http://arxiv.org/abs/1807.03135},
	abstract = {Detection of cell nuclei in microscopic images is a challenging research topic, because of limitations in cellular image quality and diversity of nuclear morphology, i.e. varying nuclei shapes, sizes, and overlaps between multiple cell nuclei. This has been a topic of enduring interest with promising recent success shown by deep learning methods. These methods train for example convolutional neural networks (CNNs) with a training set of input images and known, labeled nuclei locations. Many of these methods are supplemented by spatial or morphological processing. We develop a new approach that we call Shape Priors with Convolutional Neural Networks (SP-CNN) to perform significantly enhanced nuclei detection. A set of canonical shapes is prepared with the help of a domain expert. Subsequently, we present a new network structure that can incorporate `expected behavior' of nucleus shapes via two components: \{{\textbackslash}em learnable\} layers that perform the nucleus detection and a \{{\textbackslash}em fixed\} processing part that guides the learning with prior information. Analytically, we formulate a new regularization term that is targeted at penalizing false positives while simultaneously encouraging detection inside cell nucleus boundary. Experimental results on a challenging dataset reveal that SP-CNN is competitive with or outperforms several state-of-the-art methods.},
	urldate = {2020-03-23},
	journal = {arXiv:1807.03135 [cs, stat]},
	author = {Tofighi, Mohammad and Guo, Tiantong and Vanamala, Jairam K. P. and Monga, Vishal},
	month = jun,
	year = {2018},
	note = {arXiv: 1807.03135},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning}
}

@article{puntmann_society_2018,
	title = {Society for {Cardiovascular} {Magnetic} {Resonance} ({SCMR}) expert consensus for {CMR} imaging endpoints in clinical research: part {I} - analytical validation and clinical qualification},
	volume = {20},
	issn = {1532-429X},
	shorttitle = {Society for {Cardiovascular} {Magnetic} {Resonance} ({SCMR}) expert consensus for {CMR} imaging endpoints in clinical research},
	doi = {10.1186/s12968-018-0484-5},
	abstract = {Cardiovascular disease remains a leading cause of morbidity and mortality globally. Changing natural history of the disease due to improved care of acute conditions and ageing population necessitates new strategies to tackle conditions which have more chronic and indolent course. These include an increased deployment of safe screening methods, life-long surveillance, and monitoring of both disease activity and tailored-treatment, by way of increasingly personalized medical care. Cardiovascular magnetic resonance (CMR) is a non-invasive, ionising radiation-free method, which can support a significant number of clinically relevant measurements and offers new opportunities to advance the state of art of diagnosis, prognosis and treatment. The objective of the SCMR Clinical Trial Taskforce was to summarizes the evidence to emphasize where currently CMR-guided clinical care can indeed translate into meaningful use and efficient deployment of resources results in meaningful and efficient use. The objective of the present initiative was to provide an appraisal of evidence on analytical validation, including the accuracy and precision, and clinical qualification of parameters in disease context, clarifying the strengths and weaknesses of the state of art, as well as the gaps in the current evidence This paper is complementary to the existing position papers on standardized acquisition and post-processing ensuring robustness and transferability for widespread use. Themed imaging-endpoint guidance on trial design to support drug-discovery or change in clinical practice (part II), will be presented in a follow-up paper in due course. As CMR continues to undergo rapid development, regular updates of the present recommendations are foreseen.},
	language = {eng},
	number = {1},
	journal = {Journal of Cardiovascular Magnetic Resonance: Official Journal of the Society for Cardiovascular Magnetic Resonance},
	author = {Puntmann, Valentina O. and Valbuena, Silvia and Hinojar, Rocio and Petersen, Steffen E. and Greenwood, John P. and Kramer, Christopher M. and Kwong, Raymond Y. and McCann, Gerry P. and Berry, Colin and Nagel, Eike and {SCMR Clinical Trial Writing Group}},
	year = {2018},
	pmid = {30231886},
	pmcid = {PMC6147157},
	keywords = {Biomarker, Cardiac magnetic resonance, Cardiovascular Diseases, Consensus, Evidence-Based Medicine, Humans, Imaging, Magnetic Resonance Imaging, Position paper, Predictive Value of Tests, Prognosis, Reproducibility of Results, SCMR},
	pages = {67}
}

@inproceedings{isensee_automatic_2018,
	series = {Lecture {Notes} in {Computer} {Science}},
	title = {Automatic {Cardiac} {Disease} {Assessment} on cine-{MRI} via {Time}-{Series} {Segmentation} and {Domain} {Specific} {Features}},
	isbn = {978-3-319-75541-0},
	doi = {10.1007/978-3-319-75541-0_13},
	abstract = {Cardiac magnetic resonance imaging improves on diagnosis of cardiovascular diseases by providing images at high spatiotemporal resolution. Manual evaluation of these time-series, however, is expensive and prone to biased and non-reproducible outcomes. In this paper, we present a method that addresses named limitations by integrating segmentation and disease classification into a fully automatic processing pipeline. We use an ensemble of UNet inspired architectures for segmentation of cardiac structures such as the left and right ventricular cavity (LVC, RVC) and the left ventricular myocardium (LVM) on each time instance of the cardiac cycle. For the classification task, information is extracted from the segmented time-series in form of comprehensive features handcrafted to reflect diagnostic clinical procedures. Based on these features we train an ensemble of heavily regularized multilayer perceptrons (MLP) and a random forest classifier to predict the pathologic target class. We evaluated our method on the ACDC dataset (4 pathology groups, 1 healthy group) and achieve dice scores of 0.945 (LVC), 0.908 (RVC) and 0.905 (LVM) in a cross-validation over the training set (100 cases) and 0.950 (LVC), 0.923 (RVC) and 0.911 (LVM) on the test set (50 cases). We report a classification accuracy of 94\%94\%94 {\textbackslash}\% on a training set cross-validation and 92\%92\%92{\textbackslash}\% on the test set. Our results underpin the potential of machine learning methods for accurate, fast and reproducible segmentation and computer-assisted diagnosis (CAD).},
	language = {en},
	booktitle = {Statistical {Atlases} and {Computational} {Models} of the {Heart}. {ACDC} and {MMWHS} {Challenges}},
	author = {Isensee, Fabian and Jaeger, Paul F. and Full, Peter M. and Wolf, Ivo and Engelhardt, Sandy and Maier-Hein, Klaus H.},
	year = {2018},
	keywords = {Automated cardiac diagnosis challenge, CNN, Cardiac magnetic resonance imaging, Deep learning, Disease prediction},
	pages = {120--129}
}

@misc{noauthor_automatic_nodate,
	title = {Automatic {Cardiac} {Disease} {Assessment} on cine-{MRI} via {Time}-{Series} {Segmentation} and {Domain} {Specific} {Features} {\textbar} {SpringerLink}},
	url = {https://link.springer.com/chapter/10.1007/978-3-319-75541-0_13},
	urldate = {2020-03-17}
}

@misc{noauthor_society_nodate,
	title = {Society for {Cardiovascular} {Magnetic} {Resonance} ({SCMR}) expert consensus for {CMR} imaging endpoints in clinical research: part {I} - analytical validatio... - {PubMed} - {NCBI}},
	url = {https://www.ncbi.nlm.nih.gov/pubmed/30231886},
	urldate = {2020-03-17}
}

@article{ibanez_cardiac_2019,
	title = {Cardiac {MRI} {Endpoints} in {Myocardial} {Infarction} {Experimental} and {Clinical} {Trials}: {JACC} {Scientific} {Expert} {Panel}},
	volume = {74},
	issn = {1558-3597},
	shorttitle = {Cardiac {MRI} {Endpoints} in {Myocardial} {Infarction} {Experimental} and {Clinical} {Trials}},
	doi = {10.1016/j.jacc.2019.05.024},
	abstract = {After a reperfused myocardial infarction (MI), dynamic tissue changes occur (edema, inflammation, microvascular obstruction, hemorrhage, cardiomyocyte necrosis, and ultimately replacement by fibrosis). The extension and magnitude of these changes contribute to long-term prognosis after MI. Cardiac magnetic resonance (CMR) is the gold-standard technique for noninvasive myocardial tissue characterization. CMR is also the preferred methodology for the identification of potential benefits associated with new cardioprotective strategies both in experimental and clinical trials. However, there is a wide heterogeneity in CMR methodologies used in experimental and clinical trials, including time of post-MI scan, acquisition protocols, and, more importantly, selection of endpoints. There is a need for standardization of these methodologies to improve the translation into a real clinical benefit. The main objective of this scientific expert panel consensus document is to provide recommendations for CMR endpoint selection in experimental and clinical trials based on pathophysiology and its association with hard outcomes.},
	language = {eng},
	number = {2},
	journal = {Journal of the American College of Cardiology},
	author = {Ibanez, Borja and Aletras, Anthony H. and Arai, Andrew E. and Arheden, Hakan and Bax, Jeroen and Berry, Colin and Bucciarelli-Ducci, Chiara and Croisille, Pierre and Dall'Armellina, Erica and Dharmakumar, Rohan and Eitel, Ingo and Fernández-Jiménez, Rodrigo and Friedrich, Matthias G. and García-Dorado, David and Hausenloy, Derek J. and Kim, Raymond J. and Kozerke, Sebastian and Kramer, Christopher M. and Salerno, Michael and Sánchez-González, Javier and Sanz, Javier and Fuster, Valentin},
	month = jul,
	year = {2019},
	pmid = {31296297},
	keywords = {STEMI, area at risk, clinical trial, edema, endpoint, infarct size, magnetic resonance imaging, myocardial infarction},
	pages = {238--256}
}

@incollection{montavon_gradient-based_2019,
	address = {Cham},
	series = {Lecture {Notes} in {Computer} {Science}},
	title = {Gradient-{Based} {Vs}. {Propagation}-{Based} {Explanations}: {An} {Axiomatic} {Comparison}},
	isbn = {978-3-030-28954-6},
	shorttitle = {Gradient-{Based} {Vs}. {Propagation}-{Based} {Explanations}},
	url = {https://doi.org/10.1007/978-3-030-28954-6_13},
	abstract = {Deep neural networks, once considered to be inscrutable black-boxes, are now supplemented with techniques that can explain how these models decide. This raises the question whether the produced explanations are reliable. In this chapter, we consider two popular explanation techniques, one based on gradient computation and one based on a propagation mechanism. We evaluate them using three “axiomatic” properties: conservation, continuity, and implementation invariance. These properties are tested on the overall explanation, but also at intermediate layers, where our analysis brings further insights on how the explanation is being formed.},
	language = {en},
	urldate = {2020-03-15},
	booktitle = {Explainable {AI}: {Interpreting}, {Explaining} and {Visualizing} {Deep} {Learning}},
	publisher = {Springer International Publishing},
	author = {Montavon, Grégoire},
	editor = {Samek, Wojciech and Montavon, Grégoire and Vedaldi, Andrea and Hansen, Lars Kai and Müller, Klaus-Robert},
	year = {2019},
	doi = {10.1007/978-3-030-28954-6_13},
	keywords = {Axioms, Deep neural networks, Explanations},
	pages = {253--265}
}

@article{montavon_methods_2018,
	title = {Methods for interpreting and understanding deep neural networks},
	volume = {73},
	issn = {1051-2004},
	url = {http://www.sciencedirect.com/science/article/pii/S1051200417302385},
	doi = {10.1016/j.dsp.2017.10.011},
	abstract = {This paper provides an entry point to the problem of interpreting a deep neural network model and explaining its predictions. It is based on a tutorial given at ICASSP 2017. As a tutorial paper, the set of methods covered here is not exhaustive, but sufficiently representative to discuss a number of questions in interpretability, technical challenges, and possible applications. The second part of the tutorial focuses on the recently proposed layer-wise relevance propagation (LRP) technique, for which we provide theory, recommendations, and tricks, to make most efficient use of it on real data.},
	language = {en},
	urldate = {2020-03-14},
	journal = {Digital Signal Processing},
	author = {Montavon, Grégoire and Samek, Wojciech and Müller, Klaus-Robert},
	month = feb,
	year = {2018},
	keywords = {Activation maximization, Deep neural networks, Layer-wise relevance propagation, Sensitivity analysis, Taylor decomposition},
	pages = {1--15}
}

@article{vinogradova_towards_2020,
	title = {Towards {Interpretable} {Semantic} {Segmentation} via {Gradient}-weighted {Class} {Activation} {Mapping}},
	url = {http://arxiv.org/abs/2002.11434},
	abstract = {Convolutional neural networks have become state-of-the-art in a wide range of image recognition tasks. The interpretation of their predictions, however, is an active area of research. Whereas various interpretation methods have been suggested for image classification, the interpretation of image segmentation still remains largely unexplored. To that end, we propose SEG-GRAD-CAM, a gradient-based method for interpreting semantic segmentation. Our method is an extension of the widely-used Grad-CAM method, applied locally to produce heatmaps showing the relevance of individual pixels for semantic segmentation.},
	urldate = {2020-03-14},
	journal = {arXiv:2002.11434 [cs, eess]},
	author = {Vinogradova, Kira and Dibrov, Alexandr and Myers, Gene},
	month = feb,
	year = {2020},
	note = {arXiv: 2002.11434},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing}
}

@article{higgins_towards_2018,
	title = {Towards a {Definition} of {Disentangled} {Representations}},
	url = {http://arxiv.org/abs/1812.02230},
	abstract = {How can intelligent agents solve a diverse set of tasks in a data-efficient manner? The disentangled representation learning approach posits that such an agent would benefit from separating out (disentangling) the underlying structure of the world into disjoint parts of its representation. However, there is no generally agreed-upon definition of disentangling, not least because it is unclear how to formalise the notion of world structure beyond toy datasets with a known ground truth generative process. Here we propose that a principled solution to characterising disentangled representations can be found by focusing on the transformation properties of the world. In particular, we suggest that those transformations that change only some properties of the underlying world state, while leaving all other properties invariant, are what gives exploitable structure to any kind of data. Similar ideas have already been successfully applied in physics, where the study of symmetry transformations has revolutionised the understanding of the world structure. By connecting symmetry transformations to vector representations using the formalism of group and representation theory we arrive at the first formal definition of disentangled representations. Our new definition is in agreement with many of the current intuitions about disentangling, while also providing principled resolutions to a number of previous points of contention. While this work focuses on formally defining disentangling - as opposed to solving the learning problem - we believe that the shift in perspective to studying data transformations can stimulate the development of better representation learning algorithms.},
	urldate = {2020-03-14},
	journal = {arXiv:1812.02230 [cs, stat]},
	author = {Higgins, Irina and Amos, David and Pfau, David and Racaniere, Sebastien and Matthey, Loic and Rezende, Danilo and Lerchner, Alexander},
	month = dec,
	year = {2018},
	note = {arXiv: 1812.02230},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning}
}

@inproceedings{safarjalani_diabetic_2018,
	title = {Diabetic {Retinal} {Tomographical} {Image} {Classification} {Using} {Convolutionnal} {Neural} {Network}},
	doi = {10.1109/ICIAS.2018.8540616},
	abstract = {Diabetic Macular Edema (DME) is the most common cause of permanent vision loss among people with diabetic retinopathy. However, early detection and treatment can reduce the risk of blindness. This paper presents an automatic method to detect DME and DR and oversteps the subjective manual evaluation of opthalmologists. Based on Convolutional Neural Network, a proposed end-to-end CNN model is presented and fully trained for the automatic classification of Optical Coherence Tomography (OCT) retinal imaging. The experiments over two datasets, provided by different institutions, have been evaluated by randomly shuffling and separating the training data along with test data. Using the proposed model, the experiment results showed a classification accuracy, sensitivity and specificity of 99.02\%.},
	booktitle = {2018 {International} {Conference} on {Intelligent} and {Advanced} {System} ({ICIAS})},
	author = {Safarjalani, Rami and Sidibé, Désiré and Ainouz, Samia and Shahin, Ahmad and Mériaudeau, Fabrice},
	month = aug,
	year = {2018},
	note = {ISSN: null},
	keywords = {DME, Diabetes, Feature extraction, Noise reduction, Sensitivity, Testing, Training, automatic classification, automatic method, biomedical optical imaging, classification accuracy, convolutional neural network, convolutionnal Neural Network, diabetic macular edema, diabetic retinal tomographical image classification, diabetic retinopathy, diseases, early detection, end-to-end CNN model, eye, image classification, medical image processing, neural nets, optical coherence tomography retinal imaging, optical tomography, permanent vision loss, training data, vision defects},
	pages = {1--6}
}

@article{payer_integrating_2019,
	title = {Integrating spatial configuration into heatmap regression based {CNNs} for landmark localization},
	volume = {54},
	issn = {1361-8415},
	url = {http://www.sciencedirect.com/science/article/pii/S1361841518305784},
	doi = {10.1016/j.media.2019.03.007},
	abstract = {In many medical image analysis applications, only a limited amount of training data is available due to the costs of image acquisition and the large manual annotation effort required from experts. Training recent state-of-the-art machine learning methods like convolutional neural networks (CNNs) from small datasets is a challenging task. In this work on anatomical landmark localization, we propose a CNN architecture that learns to split the localization task into two simpler sub-problems, reducing the overall need for large training datasets. Our fully convolutional SpatialConfiguration-Net (SCN) learns this simplification due to multiplying the heatmap predictions of its two components and by training the network in an end-to-end manner. Thus, the SCN dedicates one component to locally accurate but ambiguous candidate predictions, while the other component improves robustness to ambiguities by incorporating the spatial configuration of landmarks. In our extensive experimental evaluation, we show that the proposed SCN outperforms related methods in terms of landmark localization error on a variety of size-limited 2D and 3D landmark localization datasets, i.e., hand radiographs, lateral cephalograms, hand MRIs, and spine CTs.},
	language = {en},
	urldate = {2020-03-13},
	journal = {Medical Image Analysis},
	author = {Payer, Christian and Štern, Darko and Bischof, Horst and Urschler, Martin},
	month = may,
	year = {2019},
	keywords = {Anatomical landmarks, Fully convolutional networks, Heatmap regression, Localization},
	pages = {207--219}
}



@incollection{frangi_deep_2018,
	address = {Cham},
	title = {A {Deep} {Model} with {Shape}-{Preserving} {Loss} for {Gland} {Instance} {Segmentation}},
	volume = {11071},
	isbn = {978-3-030-00933-5 978-3-030-00934-2},
	url = {http://link.springer.com/10.1007/978-3-030-00934-2_16},
	abstract = {Segmenting gland instance in histology images requires not only separating glands from a complex background but also identifying each gland individually via accurate boundary detection. This is a very challenging task due to lots of noises from the background, tiny gaps between adjacent glands, and the “coalescence” problem arising from adhesive gland instances. State-of-the-art methods adopted multichannel/multi-task deep models to separately accomplish pixel-wise gland segmentation and boundary detection, yielding a high model complexity and diﬃculties in training. In this paper, we present a uniﬁed deep model with a new shape-preserving loss which facilities the training for both pixel-wise gland segmentation and boundary detection simultaneously. The proposed shape-preserving loss helps signiﬁcantly reduce the model complexity and make the training process more controllable. Compared with the current state-of-the-art methods, the proposed deep model with the shape-preserving loss achieves the best overall performance on the 2015 MICCAI Gland Challenge dataset. In addition, the ﬂexibility of integrating the proposed shape-preserving loss into any learning based medical image segmentation networks oﬀers great potential for further performance improvement of other applications.},
	language = {en},
	urldate = {2020-03-13},
	booktitle = {Medical {Image} {Computing} and {Computer} {Assisted} {Intervention} – {MICCAI} 2018},
	publisher = {Springer International Publishing},
	author = {Yan, Zengqiang and Yang, Xin and Cheng, Kwang-Ting Tim},
	editor = {Frangi, Alejandro F. and Schnabel, Julia A. and Davatzikos, Christos and Alberola-López, Carlos and Fichtinger, Gabor},
	year = {2018},
	doi = {10.1007/978-3-030-00934-2_16},
	note = {Series Title: Lecture Notes in Computer Science},
	pages = {138--146}
}



@incollection{vemuri_joint_2003,
	address = {New York, NY},
	title = {Joint {Image} {Registration} and {Segmentation}},
	isbn = {978-0-387-21810-6},
	url = {https://doi.org/10.1007/0-387-21810-6_14},
	abstract = {Image segmentation is ubiqitous in computer vision and image processing. In some applications such as in medical imaging, the problem may be very complex due to lack of sufficient image contrast, signal to noise ratio, volume avaraging, inhomogenities caused due to nonuniform magnetic field — in the case of MRI data sets, and sometimes lack of any real boundary due to the desired shape blending into the surrounding tissue. Such scenarios pose a formidable segmentation problem and provide motivation for incoporation of prior information in to the segmentation algorithms. There are several ways to incorporate priors into the segmentation formulation and we choose to use an atlas-based segmentation technique which requires that the given atlas shape/image be registered with the target image in order to find the desired shape segmentation in the target image thus requiring simultaneous registration and segmentation to be achieved. This is a natural choice for medical applications pertinent to some of the aforementioned scenarios. In this chapter, two approaches are presented, one is a pure PDE-based formulation and the other is a vriational formulation involving a level-set representation of the shape being segmented. The former approach copes with 3D non-rigid motion and the later although easily applicable to the non-rigid motion case is shown here only for the 2D rigid motion case. Efficient numerical methods are used in both the approaches and the performance of the algorithms is depicted via examples on synthetic and real 3D and 2D image data.},
	language = {en},
	urldate = {2020-03-11},
	booktitle = {Geometric {Level} {Set} {Methods} in {Imaging}, {Vision}, and {Graphics}},
	publisher = {Springer},
	author = {Vemuri, Baba and Chen, Yunmei},
	editor = {Osher, Stanley and Paragios, Nikos},
	year = {2003},
	doi = {10.1007/0-387-21810-6_14},
	keywords = {Active Contour, Active Contour Model, Initial Contour, Prior Shape, Target Image},
	pages = {251--269}
}

@inproceedings{moeskops_deep_2016,
	address = {Cham},
	series = {Lecture {Notes} in {Computer} {Science}},
	title = {Deep {Learning} for {Multi}-task {Medical} {Image} {Segmentation} in {Multiple} {Modalities}},
	isbn = {978-3-319-46723-8},
	doi = {10.1007/978-3-319-46723-8_55},
	abstract = {Automatic segmentation of medical images is an important task for many clinical applications. In practice, a wide range of anatomical structures are visualised using different imaging modalities. In this paper, we investigate whether a single convolutional neural network (CNN) can be trained to perform different segmentation tasks.A single CNN is trained to segment six tissues in MR brain images, the pectoral muscle in MR breast images, and the coronary arteries in cardiac CTA. The CNN therefore learns to identify the imaging modality, the visualised anatomical structures, and the tissue classes.For each of the three tasks (brain MRI, breast MRI and cardiac CTA), this combined training procedure resulted in a segmentation performance equivalent to that of a CNN trained specifically for that task, demonstrating the high capacity of CNN architectures. Hence, a single system could be used in clinical practice to automatically perform diverse segmentation tasks without task-specific training.},
	language = {en},
	booktitle = {Medical {Image} {Computing} and {Computer}-{Assisted} {Intervention} – {MICCAI} 2016},
	publisher = {Springer International Publishing},
	author = {Moeskops, Pim and Wolterink, Jelmer M. and van der Velden, Bas H. M. and Gilhuijs, Kenneth G. A. and Leiner, Tim and Viergever, Max A. and Išgum, Ivana},
	editor = {Ourselin, Sebastien and Joskowicz, Leo and Sabuncu, Mert R. and Unal, Gozde and Wells, William},
	year = {2016},
	keywords = {Brain MRI, Breast MRI, Cardiac CTA, Convolutional neural networks, Deep learning, Medical image segmentation},
	pages = {478--486}
}

@article{ciarlet_orientation-preserving_2013,
	title = {Orientation-preserving condition and polyconvexity on a surface: {Application} to nonlinear shell theory},
	volume = {99},
	issn = {0021-7824},
	shorttitle = {Orientation-preserving condition and polyconvexity on a surface},
	url = {http://www.sciencedirect.com/science/article/pii/S0021782412001420},
	doi = {10.1016/j.matpur.2012.10.006},
	abstract = {We propose in this paper a definition of a “polyconvex function on a surface”, inspired by the definitions set forth in other contexts by J. Ball (1977) [3] and by J. Ball, J.C. Currie, and P.J. Olver (1981) [5]. When the surface is thought of as the middle surface of a nonlinearly elastic shell and the function as its stored energy function, we show that it is possible to assume in addition that this function is coercive for appropriate Sobolev norms and that it satisfies specific growth conditions that prevent the vectors of the covariant bases along the deformed middle surface to become linearly dependent, a condition that is the “surface analogue” of the orientation-preserving condition of J. Ball. We then show that a functional with such a polyconvex integrand is weakly lower semi-continuous, a property which eventually allows to establish the existence of minimizers. We also indicate how this new approach compares with the classical nonlinear shell theories, such as those of W.T. Koiter and P.M. Naghdi.
Résumé
On propose dans cet article une définition de “fonction polyconvexe sur une surface”, inspirée des définitions proposées par J. Ball (1977) [3] et par J. Ball, J.C. Currie, et P.J. Olver (1981) [5] dans dʼautres contextes. Quand la surface est vue comme la surface moyenne dʼune coque non linéairement élastique et la fonction comme sa densité dʼénergie, on montre quʼil est loisible de supposer en plus que cette fonction est coercive pour des normes de Sobolev convenables et quʼelle satisfait des conditions de croissance particulières qui empêchent les vecteurs des bases covariantes le long de la surface déformée de devenir linéairement dépendants, une condition qui est “lʼanalogue pour une surface” de la condition de préservation de lʼorientation de J. Ball. On montre ensuite quʼune fonctionnelle avec une telle intégrande polyconvexe est faiblement semi-continue inférieurement, une propriété qui finalement permet dʼétablir lʼexistence de minimiseurs. On indique également comment cette approche se compare aux théories non linéaires classiques des coques, telle que celles de W.T. Koiter ou P.M. Naghdi.},
	language = {en},
	number = {6},
	urldate = {2020-03-09},
	journal = {Journal de Mathématiques Pures et Appliquées},
	author = {Ciarlet, Philippe and Gogu, Radu and Mardare, Cristinel},
	month = jun,
	year = {2013},
	keywords = {Calculus of variations, Nonlinear shell theory, Polyconvexity},
	pages = {704--725}
}

@article{glowinski_numerical_1984,
	title = {Numerical {Solution} of {Problems} in {Incompressible} {Finite} {Elasticity} by {Augmented} {Lagrangian} {Methods} {II}. {Three}-{Dimensional} {Problems}},
	volume = {44},
	issn = {0036-1399},
	url = {https://epubs.siam.org/doi/abs/10.1137/0144051},
	doi = {10.1137/0144051},
	abstract = {In a first paper [SIAM J. Appl. Math., 42 (1982), pp. 400–429] we introduced an augmented Lagrangian methodology for solving equilibrium problems in incompressible finite elasticity, and then applied it to the solution of several two-dimensional and axisymmetric test problems. In this second paper we extend our methods to the solution of genuine three-dimensional problems; actually using these augmented Lagrangian techniques we can reduce the solution of these complicated nonlinear, nonconvex problems, to the solution of a sequence of linear problems and also to the solution of a sequence of minimization problems for convex functionals on convex sets. The capabilities of our methods are illustrated by the results of numerical experiments.},
	number = {4},
	urldate = {2020-03-09},
	journal = {SIAM Journal on Applied Mathematics},
	author = {Glowinski, R. and Le Tallec, P.},
	month = aug,
	year = {1984},
	pages = {710--733}
}

@article{larrazabal_anatomical_2019,
	title = {Anatomical {Priors} for {Image} {Segmentation} via {Post}-{Processing} with {Denoising} {Autoencoders}},
	url = {http://arxiv.org/abs/1906.02343},
	abstract = {Deep convolutional neural networks (CNN) proved to be highly accurate to perform anatomical segmentation of medical images. However, some of the most popular CNN architectures for image segmentation still rely on post-processing strategies (e.g. Conditional Random Fields) to incorporate connectivity constraints into the resulting masks. These post-processing steps are based on the assumption that objects are usually continuous and therefore nearby pixels should be assigned the same object label. Even if it is a valid assumption in general, these methods do not offer a straightforward way to incorporate more complex priors like convexity or arbitrary shape restrictions. In this work we propose Post-DAE, a post-processing method based on denoising autoencoders (DAE) trained using only segmentation masks. We learn a low-dimensional space of anatomically plausible segmentations, and use it as a post-processing step to impose shape constraints on the resulting masks obtained with arbitrary segmentation methods. Our approach is independent of image modality and intensity information since it employs only segmentation masks for training. This enables the use of anatomical segmentations that do not need to be paired with intensity images, making the approach very flexible. Our experimental results on anatomical segmentation of X-ray images show that Post-DAE can improve the quality of noisy and incorrect segmentation masks obtained with a variety of standard methods, by bringing them back to a feasible space, with almost no extra computational time.},
	urldate = {2020-02-21},
	journal = {arXiv:1906.02343 [cs, eess]},
	author = {Larrazabal, Agostina J. and Martinez, Cesar and Ferrante, Enzo},
	month = jun,
	year = {2019},
	note = {arXiv: 1906.02343},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing}
}



@inproceedings{larrazabal_anatomical_2019-1,
	address = {Cham},
	series = {Lecture {Notes} in {Computer} {Science}},
	title = {Anatomical {Priors} for {Image} {Segmentation} via {Post}-processing with {Denoising} {Autoencoders}},
	isbn = {978-3-030-32226-7},
	doi = {10.1007/978-3-030-32226-7_65},
	abstract = {Deep convolutional neural networks (CNN) proved to be highly accurate to perform anatomical segmentation of medical images. However, some of the most popular CNN architectures for image segmentation still rely on post-processing strategies (e.g. Conditional Random Fields) to incorporate connectivity constraints into the resulting masks. These post-processing steps are based on the assumption that objects are usually continuous and therefore nearby pixels should be assigned the same object label. Even if it is a valid assumption in general, these methods do not offer a straightforward way to incorporate more complex priors like convexity or arbitrary shape restrictions.In this work we propose Post-DAE, a post-processing method based on denoising autoencoders (DAE) trained using only segmentation masks. We learn a low-dimensional space of anatomically plausible segmentations, and use it as a post-processing step to impose shape constraints on the resulting masks obtained with arbitrary segmentation methods. Our approach is independent of image modality and intensity information since it employs only segmentation masks for training. This enables the use of anatomical segmentations that do not need to be paired with intensity images, making the approach very flexible. Our experimental results on anatomical segmentation of X-ray images show that Post-DAE can improve the quality of noisy and incorrect segmentation masks obtained with a variety of standard methods, by bringing them back to a feasible space, with almost no extra computational time.},
	language = {en},
	booktitle = {Medical {Image} {Computing} and {Computer} {Assisted} {Intervention} – {MICCAI} 2019},
	publisher = {Springer International Publishing},
	author = {Larrazabal, Agostina J. and Martinez, Cesar and Ferrante, Enzo},
	editor = {Shen, Dinggang and Liu, Tianming and Peters, Terry M. and Staib, Lawrence H. and Essert, Caroline and Zhou, Sean and Yap, Pew-Thian and Khan, Ali},
	year = {2019},
	keywords = {Anatomical segmentation, Autoencoders, Convolutional neural networks, Learning representations, Post-processing},
	pages = {585--593}
}

@misc{noauthor_non-local_nodate,
	title = {Non-local {Deep} {Features} for {Salient} {Object} {Detection} - {IEEE} {Conference} {Publication}},
	url = {https://ieeexplore.ieee.org/document/8100181/algorithms},
	urldate = {2020-02-21}
}

@misc{noauthor_cnn_2019,
	title = {{CNN} {Heat} {Maps}: {Gradients} vs. {DeconvNets} vs. {Guided} {Backpropagation}},
	shorttitle = {{CNN} {Heat} {Maps}},
	url = {https://glassboxmedicine.com/2019/10/06/cnn-heat-maps-gradients-vs-deconvnets-vs-guided-backpropagation/},
	abstract = {This post summarizes three closely related methods for creating saliency maps: Gradients (2013), DeconvNets (2014), and Guided Backpropagation (2014). Saliency maps are heat maps that are intended …},
	language = {en},
	urldate = {2020-02-20},
	journal = {Glass Box},
	month = oct,
	year = {2019}
}

@article{wang_non-local_2018,
	title = {Non-local {Neural} {Networks}},
	url = {http://arxiv.org/abs/1711.07971},
	abstract = {Both convolutional and recurrent operations are building blocks that process one local neighborhood at a time. In this paper, we present non-local operations as a generic family of building blocks for capturing long-range dependencies. Inspired by the classical non-local means method in computer vision, our non-local operation computes the response at a position as a weighted sum of the features at all positions. This building block can be plugged into many computer vision architectures. On the task of video classification, even without any bells and whistles, our non-local models can compete or outperform current competition winners on both Kinetics and Charades datasets. In static image recognition, our non-local models improve object detection/segmentation and pose estimation on the COCO suite of tasks. Code is available at https://github.com/facebookresearch/video-nonlocal-net .},
	urldate = {2020-02-19},
	journal = {arXiv:1711.07971 [cs]},
	author = {Wang, Xiaolong and Girshick, Ross and Gupta, Abhinav and He, Kaiming},
	month = apr,
	year = {2018},
	note = {arXiv: 1711.07971},
	keywords = {Computer Science - Computer Vision and Pattern Recognition}
}

@article{mcclure_knowing_2019,
	title = {Knowing what you know in brain segmentation using {Bayesian} deep neural networks},
	url = {http://arxiv.org/abs/1812.01719},
	abstract = {In this paper, we describe a Bayesian deep neural network (DNN) for predicting FreeSurfer segmentations of structural MRI volumes, in minutes rather than hours. The network was trained and evaluated on a large dataset (n = 11,480), obtained by combining data from more than a hundred different sites, and also evaluated on another completely held-out dataset (n = 418). The network was trained using a novel spike-and-slab dropout-based variational inference approach. We show that, on these datasets, the proposed Bayesian DNN outperforms previously proposed methods, in terms of the similarity between the segmentation predictions and the FreeSurfer labels, and the usefulness of the estimate uncertainty of these predictions. In particular, we demonstrated that the prediction uncertainty of this network at each voxel is a good indicator of whether the network has made an error and that the uncertainty across the whole brain can predict the manual quality control ratings of a scan. The proposed Bayesian DNN method should be applicable to any new network architecture for addressing the segmentation problem.},
	urldate = {2020-02-19},
	journal = {arXiv:1812.01719 [cs, stat]},
	author = {McClure, Patrick and Rho, Nao and Lee, John A. and Kaczmarzyk, Jakub R. and Zheng, Charles and Ghosh, Satrajit S. and Nielson, Dylan and Thomas, Adam G. and Bandettini, Peter and Pereira, Francisco},
	month = sep,
	year = {2019},
	note = {arXiv: 1812.01719},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning}
}

@article{elton_self-explaining_2020,
	title = {Self-explaining {AI} as an alternative to interpretable {AI}},
	url = {http://arxiv.org/abs/2002.05149},
	abstract = {The ability to explain decisions made by AI systems is highly sought after, especially in domains where human lives are at stake such as medicine or autonomous vehicles. While it is always possible to approximate the input-output relations of deep neural networks with human-understandable rules, the discovery of the double descent phenomena suggests that no such approximation will ever map onto the actual functioning of deep neural networks. Double descent indicates that deep neural networks typically operate by smoothly interpolating between data points rather than by extracting a few high level rules. As a result neural networks trained on complex real world data are inherently hard to interpret and prone to failure if used outside their domain of applicability. To show how we might be able to trust AI despite these problems, we introduce the concept of self-explaining AI. Self-explaining AIs are capable of providing a human-understandable explanation of each decision along with confidence levels for both the decision and explanation. Some difficulties to this approach along with possible solutions are sketched. Finally, we argue it is also important that AI systems warn their user when they are asked to perform outside their domain of applicability.},
	urldate = {2020-02-19},
	journal = {arXiv:2002.05149 [cs, stat]},
	author = {Elton, Daniel C.},
	month = feb,
	year = {2020},
	note = {arXiv: 2002.05149
version: 2},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computers and Society, Computer Science - Machine Learning, I.2.1, I.2.m, Statistics - Machine Learning}
}

@article{wang_automatic_2019,
	title = {Automatic {Segmentation} of {Vestibular} {Schwannoma} from {T2}-{Weighted} {MRI} by {Deep} {Spatial} {Attention} with {Hardness}-{Weighted} {Loss}},
	volume = {11765},
	url = {http://arxiv.org/abs/1906.03906},
	doi = {10.1007/978-3-030-32245-8_30},
	abstract = {Automatic segmentation of vestibular schwannoma (VS) tumors from magnetic resonance imaging (MRI) would facilitate efficient and accurate volume measurement to guide patient management and improve clinical workflow. The accuracy and robustness is challenged by low contrast, small target region and low through-plane resolution. We introduce a 2.5D convolutional neural network (CNN) able to exploit the different in-plane and through-plane resolutions encountered in standard of care imaging protocols. We use an attention module to enable the CNN to focus on the small target and propose a supervision on the learning of attention maps for more accurate segmentation. Additionally, we propose a hardness-weighted Dice loss function that gives higher weights to harder voxels to boost the training of CNNs. Experiments with ablation studies on the VS tumor segmentation task show that: 1) the proposed 2.5D CNN outperforms its 2D and 3D counterparts, 2) our supervised attention mechanism outperforms unsupervised attention, 3) the voxel-level hardness-weighted Dice loss can improve the performance of CNNs. Our method achieved an average Dice score and ASSD of 0.87 and 0.43{\textasciitilde}mm respectively. This will facilitate patient management decisions in clinical practice.},
	urldate = {2020-02-18},
	journal = {arXiv:1906.03906 [cs, eess]},
	author = {Wang, Guotai and Shapey, Jonathan and Li, Wenqi and Dorent, Reuben and Demitriadis, Alex and Bisdas, Sotirios and Paddick, Ian and Bradford, Robert and Ourselin, Sebastien and Vercauteren, Tom},
	year = {2019},
	note = {arXiv: 1906.03906},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing},
	pages = {264--272}
}

@article{yu_recurrent_2018,
	title = {Recurrent {Saliency} {Transformation} {Network}: {Incorporating} {Multi}-{Stage} {Visual} {Cues} for {Small} {Organ} {Segmentation}},
	shorttitle = {Recurrent {Saliency} {Transformation} {Network}},
	url = {http://arxiv.org/abs/1709.04518},
	abstract = {We aim at segmenting small organs (e.g., the pancreas) from abdominal CT scans. As the target often occupies a relatively small region in the input image, deep neural networks can be easily confused by the complex and variable background. To alleviate this, researchers proposed a coarse-to-fine approach, which used prediction from the first (coarse) stage to indicate a smaller input region for the second (fine) stage. Despite its effectiveness, this algorithm dealt with two stages individually, which lacked optimizing a global energy function, and limited its ability to incorporate multi-stage visual cues. Missing contextual information led to unsatisfying convergence in iterations, and that the fine stage sometimes produced even lower segmentation accuracy than the coarse stage. This paper presents a Recurrent Saliency Transformation Network. The key innovation is a saliency transformation module, which repeatedly converts the segmentation probability map from the previous iteration as spatial weights and applies these weights to the current iteration. This brings us two-fold benefits. In training, it allows joint optimization over the deep networks dealing with different input scales. In testing, it propagates multi-stage visual information throughout iterations to improve segmentation accuracy. Experiments in the NIH pancreas segmentation dataset demonstrate the state-of-the-art accuracy, which outperforms the previous best by an average of over 2\%. Much higher accuracies are also reported on several small organs in a larger dataset collected by ourselves. In addition, our approach enjoys better convergence properties, making it more efficient and reliable in practice.},
	urldate = {2020-02-18},
	journal = {arXiv:1709.04518 [cs]},
	author = {Yu, Qihang and Xie, Lingxi and Wang, Yan and Zhou, Yuyin and Fishman, Elliot K. and Yuille, Alan L.},
	month = apr,
	year = {2018},
	note = {arXiv: 1709.04518},
	keywords = {Computer Science - Computer Vision and Pattern Recognition}
}

@article{baumgartner_phiseg_2019,
	title = {{PHiSeg}: {Capturing} {Uncertainty} in {Medical} {Image} {Segmentation}},
	shorttitle = {{PHiSeg}},
	url = {http://arxiv.org/abs/1906.04045},
	abstract = {Segmentation of anatomical structures and pathologies is inherently ambiguous. For instance, structure borders may not be clearly visible or different experts may have different styles of annotating. The majority of current state-of-the-art methods do not account for such ambiguities but rather learn a single mapping from image to segmentation. In this work, we propose a novel method to model the conditional probability distribution of the segmentations given an input image. We derive a hierarchical probabilistic model, in which separate latent variables are responsible for modelling the segmentation at different resolutions. Inference in this model can be efficiently performed using the variational autoencoder framework. We show that our proposed method can be used to generate significantly more realistic and diverse segmentation samples compared to recent related work, both, when trained with annotations from a single or multiple annotators.},
	urldate = {2020-02-18},
	journal = {arXiv:1906.04045 [cs, eess, stat]},
	author = {Baumgartner, Christian F. and Tezcan, Kerem C. and Chaitanya, Krishna and Hötker, Andreas M. and Muehlematter, Urs J. and Schawkat, Khoschy and Becker, Anton S. and Donati, Olivio and Konukoglu, Ender},
	month = jul,
	year = {2019},
	note = {arXiv: 1906.04045},
	keywords = {Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing, Statistics - Machine Learning}
}

@article{hesamian_pourri_2019,
	title = {{POURRI} - {Deep} {Learning} {Techniques} for {Medical} {Image} {Segmentation}: {Achievements} and {Challenges}},
	volume = {32},
	issn = {1618-727X},
	shorttitle = {Deep {Learning} {Techniques} for {Medical} {Image} {Segmentation}},
	url = {https://doi.org/10.1007/s10278-019-00227-x},
	doi = {10.1007/s10278-019-00227-x},
	abstract = {Deep learning-based image segmentation is by now firmly established as a robust tool in image segmentation. It has been widely used to separate homogeneous areas as the first and critical component of diagnosis and treatment pipeline. In this article, we present a critical appraisal of popular methods that have employed deep-learning techniques for medical image segmentation. Moreover, we summarize the most common challenges incurred and suggest possible solutions.},
	language = {en},
	number = {4},
	urldate = {2020-02-17},
	journal = {Journal of Digital Imaging},
	author = {Hesamian, Mohammad Hesam and Jia, Wenjing and He, Xiangjian and Kennedy, Paul},
	month = aug,
	year = {2019},
	pages = {582--596}
}

@incollection{thewlis_modelling_2018,
	title = {Modelling and unsupervised learning of symmetric deformable object categories},
	url = {http://papers.nips.cc/paper/8040-modelling-and-unsupervised-learning-of-symmetric-deformable-object-categories.pdf},
	urldate = {2020-02-17},
	booktitle = {Advances in {Neural} {Information} {Processing} {Systems} 31},
	author = {Thewlis, James and Bilen, Hakan and Vedaldi, Andrea},
	editor = {Bengio, S. and Wallach, H. and Larochelle, H. and Grauman, K. and Cesa-Bianchi, N. and Garnett, R.},
	year = {2018},
	pages = {8178--8189}
}


@article{Peng2020_Discrete,
  title={Discretely-constrained deep network for weakly supervised segmentation},
  author={Jizong Peng and Hoel Kervadec and J. Dolz and I. B. Ayed and M. Pedersoli and Christian Desrosiers},
  journal={Neural networks : the official journal of the International Neural Network Society},
  year={2020},
  volume={130},
  pages={
          297-308
        }
}

@incollection{DCSegNet,
	title = {DC-SegNet: A discretely constrained deep network
for weakly supervised segmentation},
	url = {https://www.doc.ic.ac.uk/~bglocker/public/mednips2018/med-nips_2018_paper_90.pdf},
	urldate = {},
	booktitle = {},
	publisher = {},
	author = {Jizong Peng and Christian Desrosiers and Marco Pedersoli},
	editor = {},
	year = {2018},
	pages = {}
}


@article{simonyan_deep_2014,
	title = {Deep {Inside} {Convolutional} {Networks}: {Visualising} {Image} {Classification} {Models} and {Saliency} {Maps}},
	shorttitle = {Deep {Inside} {Convolutional} {Networks}},
	url = {http://arxiv.org/abs/1312.6034},
	abstract = {This paper addresses the visualisation of image classification models, learnt using deep Convolutional Networks (ConvNets). We consider two visualisation techniques, based on computing the gradient of the class score with respect to the input image. The first one generates an image, which maximises the class score [Erhan et al., 2009], thus visualising the notion of the class, captured by a ConvNet. The second technique computes a class saliency map, specific to a given image and class. We show that such maps can be employed for weakly supervised object segmentation using classification ConvNets. Finally, we establish the connection between the gradient-based ConvNet visualisation methods and deconvolutional networks [Zeiler et al., 2013].},
	urldate = {2020-02-12},
	journal = {arXiv:1312.6034 [cs]},
	author = {Simonyan, Karen and Vedaldi, Andrea and Zisserman, Andrew},
	month = apr,
	year = {2014},
	note = {arXiv: 1312.6034},
	keywords = {Computer Science - Computer Vision and Pattern Recognition}
}

@article{zhang_visual_2018,
	title = {Visual interpretability for deep learning: a survey},
	volume = {19},
	issn = {2095-9230},
	shorttitle = {Visual interpretability for deep learning},
	url = {https://doi.org/10.1631/FITEE.1700808},
	doi = {10.1631/FITEE.1700808},
	abstract = {This paper reviews recent studies in understanding neural-network representations and learning neural networks with interpretable/disentangled middle-layer representations. Although deep neural networks have exhibited superior performance in various tasks, interpretability is always Achilles’ heel of deep neural networks. At present, deep neural networks obtain high discrimination power at the cost of a low interpretability of their black-box representations. We believe that high model interpretability may help people break several bottlenecks of deep learning, e.g., learning from a few annotations, learning via human–computer communications at the semantic level, and semantically debugging network representations. We focus on convolutional neural networks (CNNs), and revisit the visualization of CNN representations, methods of diagnosing representations of pre-trained CNNs, approaches for disentangling pre-trained CNN representations, learning of CNNs with disentangled representations, and middle-to-end learning based on model interpretability. Finally, we discuss prospective trends in explainable artificial intelligence.},
	language = {en},
	number = {1},
	urldate = {2020-02-14},
	journal = {Frontiers of Information Technology \& Electronic Engineering},
	author = {Zhang, Quan-shi and Zhu, Song-chun},
	month = jan,
	year = {2018},
	keywords = {Artificial intelligence, Computer Science - Computer Vision and Pattern Recognition, Deep learning, Interpretable model, TP391},
	pages = {27--39}
}


@inproceedings{chen_learning_2019,
	address = {Long Beach, CA, USA},
	title = {Learning {Active} {Contour} {Models} for {Medical} {Image} {Segmentation}},
	isbn = {978-1-72813-293-8},
	url = {https://ieeexplore.ieee.org/document/8953484/},
	doi = {10.1109/CVPR.2019.01190},
	abstract = {Image segmentation is an important step in medical image processing and has been widely studied and developed for reﬁnement of clinical analysis and applications. New models based on deep learning have improved results but are restricted to pixel-wise tting of the segmentation map. Our aim was to tackle this limitation by developing a new model based on deep learning which takes into account the area inside as well as outside the region of interest as well as the size of boundaries during learning. Specically, we propose a new loss function which incorporates area and size information and integrates this into a dense deep learning model. We evaluated our approach on a dataset of more than 2,000 cardiac MRI scans. Our results show that the proposed loss function outperforms other mainstream loss function Cross-entropy on two common segmentation networks. Our loss function is robust while using different hyperparameter λ.},
	language = {en},
	urldate = {2020-02-17},
	booktitle = {2019 {IEEE}/{CVF} {Conference} on {Computer} {Vision} and {Pattern} {Recognition} ({CVPR})},
	publisher = {IEEE},
	author = {Chen, Xu and Williams, Bryan M. and Vallabhaneni, Srinivasa R. and Czanner, Gabriela and Williams, Rachel and Zheng, Yalin},
	month = jun,
	year = {2019},
	pages = {11624--11632}
}
@misc{noauthor_non-local_nodate,
	title = {A {Non}-local {Topology}-{Preserving} {Segmentation}-{Guided} {Registration} {Model} {\textbar} {Journal} of {Mathematical} {Imaging} and {Vision}},
	url = {https://dl.acm.org/doi/abs/10.1007/s10851-016-0699-8},
	urldate = {2020-02-16}
}

@misc{noauthor_non-local_nodate-1,
	title = {A {Non}-local {Topology}-{Preserving} {Segmentation}-{Guided} {Registration} {Model} {\textbar} {SpringerLink}},
	url = {https://link.springer.com/article/10.1007/s10851-016-0699-8},
	urldate = {2020-02-16}
}

@article{jetley_learn_2018,
	title = {Learn {To} {Pay} {Attention}},
	url = {http://arxiv.org/abs/1804.02391},
	abstract = {We propose an end-to-end-trainable attention module for convolutional neural network (CNN) architectures built for image classification. The module takes as input the 2D feature vector maps which form the intermediate representations of the input image at different stages in the CNN pipeline, and outputs a 2D matrix of scores for each map. Standard CNN architectures are modified through the incorporation of this module, and trained under the constraint that a convex combination of the intermediate 2D feature vectors, as parameterised by the score matrices, must {\textbackslash}textit\{alone\} be used for classification. Incentivised to amplify the relevant and suppress the irrelevant or misleading, the scores thus assume the role of attention values. Our experimental observations provide clear evidence to this effect: the learned attention maps neatly highlight the regions of interest while suppressing background clutter. Consequently, the proposed function is able to bootstrap standard CNN architectures for the task of image classification, demonstrating superior generalisation over 6 unseen benchmark datasets. When binarised, our attention maps outperform other CNN-based attention maps, traditional saliency maps, and top object proposals for weakly supervised segmentation as demonstrated on the Object Discovery dataset. We also demonstrate improved robustness against the fast gradient sign method of adversarial attack.},
	urldate = {2020-02-16},
	journal = {arXiv:1804.02391 [cs]},
	author = {Jetley, Saumya and Lord, Nicholas A. and Lee, Namhoon and Torr, Philip H. S.},
	month = apr,
	year = {2018},
	note = {arXiv: 1804.02391},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition}
}

@article{zintgraf_visualizing_2017,
	title = {Visualizing {Deep} {Neural} {Network} {Decisions}: {Prediction} {Difference} {Analysis}},
	shorttitle = {Visualizing {Deep} {Neural} {Network} {Decisions}},
	url = {http://arxiv.org/abs/1702.04595},
	abstract = {This article presents the prediction difference analysis method for visualizing the response of a deep neural network to a specific input. When classifying images, the method highlights areas in a given input image that provide evidence for or against a certain class. It overcomes several shortcoming of previous methods and provides great additional insight into the decision making process of classifiers. Making neural network decisions interpretable through visualization is important both to improve models and to accelerate the adoption of black-box classifiers in application areas such as medicine. We illustrate the method in experiments on natural images (ImageNet data), as well as medical images (MRI brain scans).},
	urldate = {2020-02-16},
	journal = {arXiv:1702.04595 [cs]},
	author = {Zintgraf, Luisa M. and Cohen, Taco S. and Adel, Tameem and Welling, Max},
	month = feb,
	year = {2017},
	note = {arXiv: 1702.04595},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition}
}

@article{fong_interpretable_2017,
	title = {Interpretable {Explanations} of {Black} {Boxes} by {Meaningful} {Perturbation}},
	url = {http://arxiv.org/abs/1704.03296},
	doi = {10.1109/ICCV.2017.371},
	abstract = {As machine learning algorithms are increasingly applied to high impact yet high risk tasks, such as medical diagnosis or autonomous driving, it is critical that researchers can explain how such algorithms arrived at their predictions. In recent years, a number of image saliency methods have been developed to summarize where highly complex neural networks "look" in an image for evidence for their predictions. However, these techniques are limited by their heuristic nature and architectural constraints. In this paper, we make two main contributions: First, we propose a general framework for learning different kinds of explanations for any black box algorithm. Second, we specialise the framework to find the part of an image most responsible for a classifier decision. Unlike previous works, our method is model-agnostic and testable because it is grounded in explicit and interpretable image perturbations.},
	urldate = {2020-02-16},
	journal = {2017 IEEE International Conference on Computer Vision (ICCV)},
	author = {Fong, Ruth and Vedaldi, Andrea},
	month = oct,
	year = {2017},
	note = {arXiv: 1704.03296},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning},
	pages = {3449--3457}
}

@article{sundararajan_axiomatic_2017,
	title = {Axiomatic {Attribution} for {Deep} {Networks}},
	url = {http://arxiv.org/abs/1703.01365},
	abstract = {We study the problem of attributing the prediction of a deep network to its input features, a problem previously studied by several other works. We identify two fundamental axioms---Sensitivity and Implementation Invariance that attribution methods ought to satisfy. We show that they are not satisfied by most known attribution methods, which we consider to be a fundamental weakness of those methods. We use the axioms to guide the design of a new attribution method called Integrated Gradients. Our method requires no modification to the original network and is extremely simple to implement; it just needs a few calls to the standard gradient operator. We apply this method to a couple of image models, a couple of text models and a chemistry model, demonstrating its ability to debug networks, to extract rules from a network, and to enable users to engage with models better.},
	urldate = {2020-02-16},
	journal = {arXiv:1703.01365 [cs]},
	author = {Sundararajan, Mukund and Taly, Ankur and Yan, Qiqi},
	month = jun,
	year = {2017},
	note = {arXiv: 1703.01365},
	keywords = {Computer Science - Machine Learning}
}

@article{smilkov_smoothgrad_2017,
	title = {{SmoothGrad}: removing noise by adding noise},
	shorttitle = {{SmoothGrad}},
	url = {http://arxiv.org/abs/1706.03825},
	abstract = {Explaining the output of a deep network remains a challenge. In the case of an image classifier, one type of explanation is to identify pixels that strongly influence the final decision. A starting point for this strategy is the gradient of the class score function with respect to the input image. This gradient can be interpreted as a sensitivity map, and there are several techniques that elaborate on this basic idea. This paper makes two contributions: it introduces SmoothGrad, a simple method that can help visually sharpen gradient-based sensitivity maps, and it discusses lessons in the visualization of these maps. We publish the code for our experiments and a website with our results.},
	urldate = {2020-02-16},
	journal = {arXiv:1706.03825 [cs, stat]},
	author = {Smilkov, Daniel and Thorat, Nikhil and Kim, Been and Viégas, Fernanda and Wattenberg, Martin},
	month = jun,
	year = {2017},
	note = {arXiv: 1706.03825},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning}
}

@article{adebayo_sanity_2018,
	title = {Sanity {Checks} for {Saliency} {Maps}},
	url = {http://arxiv.org/abs/1810.03292},
	abstract = {Saliency methods have emerged as a popular tool to highlight features in an input deemed relevant for the prediction of a learned model. Several saliency methods have been proposed, often guided by visual appeal on image data. In this work, we propose an actionable methodology to evaluate what kinds of explanations a given method can and cannot provide. We find that reliance, solely, on visual assessment can be misleading. Through extensive experiments we show that some existing saliency methods are independent both of the model and of the data generating process. Consequently, methods that fail the proposed tests are inadequate for tasks that are sensitive to either data or model, such as, finding outliers in the data, explaining the relationship between inputs and outputs that the model learned, and debugging the model. We interpret our findings through an analogy with edge detection in images, a technique that requires neither training data nor model. Theory in the case of a linear model and a single-layer convolutional neural network supports our experimental findings.},
	urldate = {2020-02-14},
	journal = {arXiv:1810.03292 [cs, stat]},
	author = {Adebayo, Julius and Gilmer, Justin and Muelly, Michael and Goodfellow, Ian and Hardt, Moritz and Kim, Been},
	month = oct,
	year = {2018},
	note = {arXiv: 1810.03292},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning}
}

@article{samek_towards_2019,
	title = {Towards {Explainable} {Artificial} {Intelligence}},
	volume = {11700},
	url = {http://arxiv.org/abs/1909.12072},
	doi = {10.1007/978-3-030-28954-6_1},
	abstract = {In recent years, machine learning (ML) has become a key enabling technology for the sciences and industry. Especially through improvements in methodology, the availability of large databases and increased computational power, today's ML algorithms are able to achieve excellent performance (at times even exceeding the human level) on an increasing number of complex tasks. Deep learning models are at the forefront of this development. However, due to their nested non-linear structure, these powerful models have been generally considered "black boxes", not providing any information about what exactly makes them arrive at their predictions. Since in many applications, e.g., in the medical domain, such lack of transparency may be not acceptable, the development of methods for visualizing, explaining and interpreting deep learning models has recently attracted increasing attention. This introductory paper presents recent developments and applications in this field and makes a plea for a wider use of explainable learning algorithms in practice.},
	urldate = {2020-02-14},
	journal = {arXiv:1909.12072 [cs]},
	author = {Samek, Wojciech and Müller, Klaus-Robert},
	year = {2019},
	note = {arXiv: 1909.12072},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing},
	pages = {5--22}
}



@inproceedings{zhou_prior-aware_2019,
	title = {Prior-aware {Neural} {Network} for {Partially}-{Supervised} {Multi}-{Organ} {Segmentation}},
	url = {https://arxiv.org/abs/1904.06346},
	urldate = {2019-11-19},
	booktitle = {International {Conference} on {Computer} {Vision}},
	author = {Zhou, Yuyin and Yuille, Alan and Wang, Chong and Fishman, Elliot and Han, Mei and Bai, Song and Chen, Xinlei and Li, Zhe},
	year = {2019}
}

@article{kervadec_constrained_2019,
	title = {Constrained {Deep} {Networks}: {Lagrangian} {Optimization} via {Log}-{Barrier} {Extensions}},
	shorttitle = {Constrained {Deep} {Networks}},
	url = {http://arxiv.org/abs/1904.04205},
	abstract = {This study investigates imposing inequality constraints on the outputs of CNNs, with application to weakly supervised segmentation. In the context of deep networks, constraints are commonly handled with penalties for their simplicity, and despite their well-known limitations. Lagrangian-dual optimization has been largely avoided, mainly due to the computational complexity and stability/convergence issues caused by alternating explicit dual updates/projections and stochastic optimization. Several studies showed that, for deep CNNs, the theoretical and practical advantages of Lagrangian optimization over penalties do not materialize in practice. We propose log-barrier extensions, which approximate Lagrangian optimization of constrained-CNN problems with a sequence of unconstrained losses. Unlike standard interior-point and log-barrier methods, our formulation does not need an initial feasible solution. Furthermore, we provide a new technical result, which shows that the proposed extensions yield an upper bound on the duality gap. This generalizes the duality-gap result of standard log-barriers, yielding sub-optimality certificates for feasible solutions. While sub-optimality is not guaranteed for non-convex problems, our result shows that log-barrier extensions are a principled way to approximate Lagrangian optimization for constrained CNNs via implicit dual variables. We report comprehensive constrained-CNN experiments, showing that our formulation outperforms several penalty-based methods, in terms of accuracy and training stability.},
	urldate = {2020-02-12},
	journal = {arXiv:1904.04205 [cs]},
	author = {Kervadec, Hoel and Dolz, Jose and Yuan, Jing and Desrosiers, Christian and Granger, Eric and Ayed, Ismail Ben},
	month = sep,
	year = {2019},
	note = {arXiv: 1904.04205},
	keywords = {Computer Science - Computer Vision and Pattern Recognition}
}

@inproceedings{petit_biasing_nodate,
	title = {Biasing {Deep} {ConvNets} for {Semantic} {Segmentation} of {Medical} {Images} with a {Prior}-driven {Prediction} {Function}},
	booktitle = {Medical Imaging with Deep Learning},
	author = {Petit, Olivier and Thome, Nicolas and Soler, Luc},
	year = {2019}
}
% Anatomically {Constrained} {Neural} {Networks} ({ACNNs}): {Application} to {Cardiac} {Image} {Enhancement} and {Segmentation}}


@article{levine_end--end_2016,
	title = {End-to-end {Training} of {Deep} {Visuomotor} {Policies}},
	volume = {17},
	issn = {1532-4435},
	url = {http://dl.acm.org/citation.cfm?id=2946645.2946684},
	abstract = {Policy search methods can allow robots to learn control policies for a wide range of tasks, but practical applications of policy search often require hand-engineered components for perception, state estimation, and low-level control. In this paper, we aim to answer the following question: does training the perception and control systems jointly end-to-end provide better performance than training each component separately? To this end, we develop a method that can be used to learn policies that map raw image observations directly to torques at the robot's motors. The policies are represented by deep convolutional neural networks (CNNs) with 92,000 parameters, and are trained using a guided policy search method, which transforms policy search into supervised learning, with supervision provided by a simple trajectory-centric reinforcement learning method. We evaluate our method on a range of real-world manipulation tasks that require close coordination between vision and control, such as screwing a cap onto a bottle, and present simulated comparisons to a range of prior policy search methods.},
	number = {1},
	urldate = {2019-12-17},
	journal = {J. Mach. Learn. Res.},
	author = {Levine, Sergey and Finn, Chelsea and Darrell, Trevor and Abbeel, Pieter},
	month = jan,
	year = {2016},
	keywords = {neural networks, optimal control, reinforcement learning, vision},
	pages = {1334--1373}
}

@article{storath_fast_2014,
	title = {Fast {Partitioning} of {Vector}-{Valued} {Images}},
	volume = {7},
	issn = {1936-4954},
	url = {http://epubs.siam.org/doi/10.1137/130950367},
	doi = {10.1137/130950367},
	abstract = {We propose a fast splitting approach to the classical variational formulation of the image partitioning problem, which is frequently referred to as the Potts or piecewise constant Mumford–Shah model. For vector-valued images, our approach is signiﬁcantly faster than the methods based on graph cuts and convex relaxations of the Potts model which are presently the state-of-the-art. The computational costs of our algorithm only grow linearly with the dimension of the data space which contrasts the exponential growth of the state-of-the-art methods. This allows us to process images with high-dimensional codomains such as multispectral images. Our approach produces results of a quality comparable with that of graph cuts and the convex relaxation strategies, and we do not need an a priori discretization of the label space. Furthermore, the number of partitions has almost no inﬂuence on the computational costs, which makes our algorithm also suitable for the reconstruction of piecewise constant (color or vectorial) images.},
	language = {en},
	number = {3},
	urldate = {2020-02-12},
	journal = {SIAM Journal on Imaging Sciences},
	author = {Storath, Martin and Weinmann, Andreas},
	month = jan,
	year = {2014},
	keywords = {Algorithm, Calculus of variations, Color, Computation, Cut (graph theory), Dataspaces, Discretization, Linear programming relaxation, Multispectral image, Partition problem, Potts model, Time complexity},
	pages = {1826--1852}
}

@article{locatello_challenging_2019,
	title = {Challenging {Common} {Assumptions} in the {Unsupervised} {Learning} of {Disentangled} {Representations}},
	url = {http://arxiv.org/abs/1811.12359},
	abstract = {The key idea behind the unsupervised learning of disentangled representations is that real-world data is generated by a few explanatory factors of variation which can be recovered by unsupervised learning algorithms. In this paper, we provide a sober look at recent progress in the field and challenge some common assumptions. We first theoretically show that the unsupervised learning of disentangled representations is fundamentally impossible without inductive biases on both the models and the data. Then, we train more than 12000 models covering most prominent methods and evaluation metrics in a reproducible large-scale experimental study on seven different data sets. We observe that while the different methods successfully enforce properties ``encouraged'' by the corresponding losses, well-disentangled models seemingly cannot be identified without supervision. Furthermore, increased disentanglement does not seem to lead to a decreased sample complexity of learning for downstream tasks. Our results suggest that future work on disentanglement learning should be explicit about the role of inductive biases and (implicit) supervision, investigate concrete benefits of enforcing disentanglement of the learned representations, and consider a reproducible experimental setup covering several data sets.},
	urldate = {2020-02-12},
	journal = {arXiv:1811.12359 [cs, stat]},
	author = {Locatello, Francesco and Bauer, Stefan and Lucic, Mario and Rätsch, Gunnar and Gelly, Sylvain and Schölkopf, Bernhard and Bachem, Olivier},
	month = jun,
	year = {2019},
	note = {arXiv: 1811.12359},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Statistics - Machine Learning}
}

@article{bau_network_2017,
	title = {Network {Dissection}: {Quantifying} {Interpretability} of {Deep} {Visual} {Representations}},
	shorttitle = {Network {Dissection}},
	url = {http://arxiv.org/abs/1704.05796},
	abstract = {We propose a general framework called Network Dissection for quantifying the interpretability of latent representations of CNNs by evaluating the alignment between individual hidden units and a set of semantic concepts. Given any CNN model, the proposed method draws on a broad data set of visual concepts to score the semantics of hidden units at each intermediate convolutional layer. The units with semantics are given labels across a range of objects, parts, scenes, textures, materials, and colors. We use the proposed method to test the hypothesis that interpretability of units is equivalent to random linear combinations of units, then we apply our method to compare the latent representations of various networks when trained to solve different supervised and self-supervised training tasks. We further analyze the effect of training iterations, compare networks trained with different initializations, examine the impact of network depth and width, and measure the effect of dropout and batch normalization on the interpretability of deep visual representations. We demonstrate that the proposed method can shed light on characteristics of CNN models and training methods that go beyond measurements of their discriminative power.},
	urldate = {2020-02-12},
	journal = {arXiv:1704.05796 [cs]},
	author = {Bau, David and Zhou, Bolei and Khosla, Aditya and Oliva, Aude and Torralba, Antonio},
	month = apr,
	year = {2017},
	note = {arXiv: 1704.05796},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, I.2.10}
}

@article{qin_joint_2018,
	title = {Joint {Learning} of {Motion} {Estimation} and {Segmentation} for {Cardiac} {MR} {Image} {Sequences}},
	url = {http://arxiv.org/abs/1806.04066},
	abstract = {Cardiac motion estimation and segmentation play important roles in quantitatively assessing cardiac function and diagnosing cardiovascular diseases. In this paper, we propose a novel deep learning method for joint estimation of motion and segmentation from cardiac MR image sequences. The proposed network consists of two branches: a cardiac motion estimation branch which is built on a novel unsupervised Siamese style recurrent spatial transformer network, and a cardiac segmentation branch that is based on a fully convolutional network. In particular, a joint multi-scale feature encoder is learned by optimizing the segmentation branch and the motion estimation branch simultaneously. This enables the weakly-supervised segmentation by taking advantage of features that are unsupervisedly learned in the motion estimation branch from a large amount of unannotated data. Experimental results using cardiac MRI images from 220 subjects show that the joint learning of both tasks is complementary and the proposed models outperform the competing methods significantly in terms of accuracy and speed.},
	urldate = {2020-02-12},
	journal = {arXiv:1806.04066 [cs]},
	author = {Qin, Chen and Bai, Wenjia and Schlemper, Jo and Petersen, Steffen E. and Piechnik, Stefan K. and Neubauer, Stefan and Rueckert, Daniel},
	month = jun,
	year = {2018},
	note = {arXiv: 1806.04066},
	keywords = {Computer Science - Computer Vision and Pattern Recognition}
}

@article{qin_unsupervised_2019,
	title = {Unsupervised {Deformable} {Registration} for {Multi}-{Modal} {Images} via {Disentangled} {Representations}},
	url = {http://arxiv.org/abs/1903.09331},
	abstract = {We propose a fully unsupervised multi-modal deformable image registration method (UMDIR), which does not require any ground truth deformation fields or any aligned multi-modal image pairs during training. Multi-modal registration is a key problem in many medical image analysis applications. It is very challenging due to complicated and unknown relationships between different modalities. In this paper, we propose an unsupervised learning approach to reduce the multi-modal registration problem to a mono-modal one through image disentangling. In particular, we decompose images of both modalities into a common latent shape space and separate latent appearance spaces via an unsupervised multi-modal image-to-image translation approach. The proposed registration approach is then built on the factorized latent shape code, with the assumption that the intrinsic shape deformation existing in original image domain is preserved in this latent space. Specifically, two metrics have been proposed for training the proposed network: a latent similarity metric defined in the common shape space and a learningbased image similarity metric based on an adversarial loss. We examined different variations of our proposed approach and compared them with conventional state-of-the-art multi-modal registration methods. Results show that our proposed methods achieve competitive performance against other methods at substantially reduced computation time.},
	urldate = {2020-02-12},
	journal = {arXiv:1903.09331 [cs]},
	author = {Qin, Chen and Shi, Bibo and Liao, Rui and Mansi, Tommaso and Rueckert, Daniel and Kamen, Ali},
	month = mar,
	year = {2019},
	note = {arXiv: 1903.09331},
	keywords = {Computer Science - Computer Vision and Pattern Recognition}
}

@article{elmahdy_adversarial_2019,
	title = {Adversarial optimization for joint registration and segmentation in prostate {CT} radiotherapy},
	url = {http://arxiv.org/abs/1906.12223},
	abstract = {Joint image registration and segmentation has long been an active area of research in medical imaging. Here, we reformulate this problem in a deep learning setting using adversarial learning. We consider the case in which fixed and moving images as well as their segmentations are available for training, while segmentations are not available during testing; a common scenario in radiotherapy. The proposed framework consists of a 3D end-to-end generator network that estimates the deformation vector field (DVF) between fixed and moving images in an unsupervised fashion and applies this DVF to the moving image and its segmentation. A discriminator network is trained to evaluate how well the moving image and segmentation align with the fixed image and segmentation. The proposed network was trained and evaluated on follow-up prostate CT scans for image-guided radiotherapy, where the planning CT contours are propagated to the daily CT images using the estimated DVF. A quantitative comparison with conventional registration using {\textbackslash}texttt\{elastix\} showed that the proposed method improved performance and substantially reduced computation time, thus enabling real-time contour propagation necessary for online-adaptive radiotherapy.},
	urldate = {2020-02-12},
	journal = {arXiv:1906.12223 [cs, eess]},
	author = {Elmahdy, Mohamed S. and Wolterink, Jelmer M. and Sokooti, Hessam and Išgum, Ivana and Staring, Marius},
	month = jun,
	year = {2019},
	note = {arXiv: 1906.12223},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing}
}

@inproceedings{mahapatra_joint_2018,
	title = {Joint registration and segmentation of xray images using generative adversarial networks},
	url = {https://research.monash.edu/en/publications/joint-registration-and-segmentation-of-xray-images-using-generati},
	doi = {10.1007/978-3-030-00919-9_9},
	language = {English},
	urldate = {2020-02-12},
	booktitle = {Machine {Learning} in {Medical} {Imaging}: 9th {International} {Workshop}, {MLMI} 2018, {Held} in {Conjunction} with {MICCAI} 2018, {Granada}, {Spain}, {September} 16, 2018, {Proceedings}},
	publisher = {Springer},
	author = {Mahapatra, Dwarikanath and Ge, Zongyuan and Sedai, Suman and Chakravorty, Rajib},
	month = jan,
	year = {2018},
	pages = {73--80}
}

@inproceedings{estienne_u-resnet_2019,
	address = {Shenzhen, China},
	title = {U-{ReSNet}: {Ultimate} {Coupling} of {Registration} and {Segmentation} with {Deep} {Nets}},
	shorttitle = {U-{ReSNet}},
	url = {https://hal.archives-ouvertes.fr/hal-02365899},
	doi = {10.1007/978-3-030-32248-9_35},
	abstract = {In this study, we propose a 3D deep neural network called U-ReSNet, a joint framework that can accurately register and segment medical volumes. The proposed network learns to automatically generate linear and elastic deformation models, trained by minimizing the mean square error and the local cross correlation similarity metrics. In parallel, a coupled architecture is integrated, seeking to provide segmentation maps for anatomies or tissue patterns using an additional decoder part trained with the dice coefficient metric. U-ReSNet is trained in an end to end fashion, while due to this joint optimization the generated network features are more informative leading to promising results compared to other deep learning-based methods existing in the literature. We evaluated the proposed architecture using the publicly available OASIS 3 dataset, measuring the dice coefficient metric for both registration and segmentation tasks. Our promising results indicate the potentials of our method which is composed from a convolutional architecture that is extremely simple and light in terms of parameters. Our code is publicly available https://github.com/TheoEst/coupling\_registration\_segmentation.},
	urldate = {2020-02-12},
	booktitle = {{MICCAI} 2019: {Medical} {Image} {Computing} and {Computer} {Assisted} {Intervention} – {MICCAI} 2019},
	publisher = {MICCAI},
	author = {Estienne, Théo and Vakalopoulou, Maria and Christodoulidis, Stergios and Battistella, Enzo and Lerousseau, Marvin and Carre, Alexandre and Klausner, Guillaume and Sun, Roger and Robert, Charlotte and Mougiakakou, Stavroula and Paragios, Nikos and Deutsch, Eric},
	month = oct,
	year = {2019},
	keywords = {3D Convolutional Neural Networks, Brain Tumor Segmentation, Deformable Registration, Image Registration},
	pages = {310--319}
}

@article{xu_deepatlas_2019,
	title = {{DeepAtlas}: {Joint} {Semi}-{Supervised} {Learning} of {Image} {Registration} and {Segmentation}},
	shorttitle = {{DeepAtlas}},
	url = {http://arxiv.org/abs/1904.08465},
	abstract = {Deep convolutional neural networks (CNNs) are state-of-the-art for semantic image segmentation, but typically require many labeled training samples. Obtaining 3D segmentations of medical images for supervised training is difficult and labor intensive. Motivated by classical approaches for joint segmentation and registration we therefore propose a deep learning framework that jointly learns networks for image registration and image segmentation. In contrast to previous work on deep unsupervised image registration, which showed the benefit of weak supervision via image segmentations, our approach can use existing segmentations when available and computes them via the segmentation network otherwise, thereby providing the same registration benefit. Conversely, segmentation network training benefits from the registration, which essentially provides a realistic form of data augmentation. Experiments on knee and brain 3D magnetic resonance (MR) images show that our approach achieves large simultaneous improvements of segmentation and registration accuracy (over independently trained networks) and allows training high-quality models with very limited training data. Specifically, in a one-shot-scenario (with only one manually labeled image) our approach increases Dice scores (\%) over an unsupervised registration network by 2.7 and 1.8 on the knee and brain images respectively.},
	urldate = {2020-02-12},
	journal = {arXiv:1904.08465 [cs]},
	author = {Xu, Zhenlin and Niethammer, Marc},
	month = jul,
	year = {2019},
	note = {arXiv: 1904.08465},
	keywords = {Computer Science - Computer Vision and Pattern Recognition}
}

@article{al-shedivat_contextual_2018,
	title = {Contextual {Explanation} {Networks}},
	url = {http://arxiv.org/abs/1705.10301},
	abstract = {Modern learning algorithms excel at producing accurate but complex models of the data. However, deploying such models in the real-world requires extra care: we must ensure their reliability, robustness, and absence of undesired biases. This motivates the development of models that are equally accurate but can be also easily inspected and assessed beyond their predictive performance. To this end, we introduce contextual explanation networks (CENs)---a class of architectures that learn to predict by generating and utilizing intermediate, simplified probabilistic models. Specifically, CENs generate parameters for intermediate graphical models which are further used for prediction and play the role of explanations. Contrary to the existing post-hoc model-explanation tools, CENs learn to predict and to explain jointly. Our approach offers two major advantages: (i) for each prediction, valid, instance-specific explanations are generated with no computational overhead and (ii) prediction via explanation acts as a regularizer and boosts performance in low-resource settings. We analyze the proposed framework theoretically and experimentally. Our results on image and text classification and survival analysis tasks demonstrate that CENs are not only competitive with the state-of-the-art methods but also offer additional insights behind each prediction, that are valuable for decision support. We also show that while post-hoc methods may produce misleading explanations in certain cases, CENs are always consistent and allow to detect such cases systematically.},
	urldate = {2020-02-12},
	journal = {arXiv:1705.10301 [cs, stat]},
	author = {Al-Shedivat, Maruan and Dubey, Avinava and Xing, Eric P.},
	month = dec,
	year = {2018},
	note = {arXiv: 1705.10301},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Statistics - Machine Learning}
}

@inproceedings{narayanaswamy_learning_2017,
	title = {Learning {Disentangled} {Representations} with {Semi}-{Supervised} {Deep} {Generative} {Models}},
	doi = {10.17863/CAM.42159},
	abstract = {Variational autoencoders (VAEs) learn representations of data by jointly training a probabilistic encoder and decoder network. Typically these models encode all features of the data into a single variable. Here we are interested in learning disentangled representations that encode distinct aspects of the data into separate variables. We propose to learn such representations using model architectures that generalise from standard VAEs, employing a general graphical model structure in the encoder and decoder. This allows us to train partially-specified models that make relatively strong assumptions about a subset of interpretable variables and rely on the flexibility of neural networks to learn representations for the remaining variables. We further define a general objective for semi-supervised learning in this model class, which can be approximated using an importance sampling procedure. We evaluate our framework's ability to learn disentangled representations, both by qualitative exploration of its generative capacity, and quantitative evaluation of its discriminative ability on a variety of models and datasets.},
	booktitle = {{NIPS}},
	author = {Narayanaswamy, Siddharth and Paige, Brooks and Meent, Jan-Willem van de and Desmaison, Alban and Goodman, Noah D. and Kohli, Pushmeet and Wood, Frank D. and Torr, Philip H. S.},
	year = {2017},
	keywords = {Approximation algorithm, Artificial neural network, Autoencoder, ENCODE, Encoder, Graphical model, Importance sampling, Sampling (signal processing), Semi-supervised learning, Semiconductor industry, Supervised learning}
}

@misc{patra_ariji_and_noble_j_alison_incremental_nodate,
	title = {Incremental {Learning} of {Fetal} {Heart} {Anatomies} {Using} {Interpretable} {Saliency} {Maps} {\textbar} {SpringerLink}},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-39343-4_11},
	urldate = {2020-02-12},
	author = {Patra, Ariji {and} Noble, J. Alison}
}

@article{litjens_survey_2017,
	title = {A survey on deep learning in medical image analysis},
	volume = {42},
	issn = {1361-8415},
	url = {http://www.sciencedirect.com/science/article/pii/S1361841517301135},
	doi = {10.1016/j.media.2017.07.005},
	abstract = {Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks. Concise overviews are provided of studies per application area: neuro, retinal, pulmonary, digital pathology, breast, cardiac, abdominal, musculoskeletal. We end with a summary of the current state-of-the-art, a critical discussion of open challenges and directions for future research.},
	language = {en},
	urldate = {2020-02-12},
	journal = {Medical Image Analysis},
	author = {Litjens, Geert and Kooi, Thijs and Bejnordi, Babak Ehteshami and Setio, Arnaud Arindra Adiyoso and Ciompi, Francesco and Ghafoorian, Mohsen and van der Laak, Jeroen A. W. M. and van Ginneken, Bram and Sánchez, Clara I.},
	month = dec,
	year = {2017},
	keywords = {Convolutional neural networks, Deep learning, Medical imaging, Survey},
	pages = {60--88}
}


@inproceedings{AttUNet2018,
title={Attention U-Net: Learning Where to Look for the Pancreas},
author={Ozan Oktay and Jo Schlemper and Lo{\"i}c Le Folgoc and Matthew C. H. Lee and Mattias P. Heinrich and Kazunari Misawa and Kensaku Mori and Steven G. McDonagh and Nils Y. Hammerla and Bernhard Kainz and Ben Glocker and Daniel Rueckert},
booktitle={Medical Imaging with Deep Learning},
year={2018},

}


@inproceedings{Marquez2017,
  author    = {Marquez-Neila, P., Salzmann, M., Fua, P.},
  title     = {{ Imposing Hard Constraints on Deep Networks: ´
Promises and Limitations.}},
  year      = {2017},
  maintitle = {CVPR},
  booktitle = {CVPR Workshop on Negative Results in Computer Vision},
}

@InProceedings{Dolz2017,
author="Dolz, Jose
and Ben Ayed, Ismail
and Desrosiers, Christian",
title="Unbiased Shape Compactness for Segmentation",
booktitle="MICCAI",
year="2017",
pages="755--763",
abstract="We propose to constrain segmentation functionals with a dimensionless, unbiased and position-independent shape compactness prior, which we solve efficiently with an alternating direction method of multipliers (ADMM). Involving a squared sum of pairwise potentials, our prior results in a challenging high-order optimization problem, which involves dense (fully connected) graphs. We split the problem into a sequence of easier sub-problems, each performed efficiently at each iteration: (i) a sparse-matrix inversion based on Woodbury identity, (ii) a closed-form solution of a cubic equation and (iii) a graph-cut update of a sub-modular pairwise sub-problem with a sparse graph. We deploy our prior in an energy minimization, in conjunction with a supervised classifier term based on CNNs and standard regularization constraints. We demonstrate the usefulness of our energy in several medical applications. In particular, we report comprehensive evaluations of our fully automated algorithm over 40 subjects, showing a competitive performance for the challenging task of abdominal aorta segmentation in MRI.",
isbn="978-3-319-66182-7"
}



@misc{kervadec2019constrainedLogBarrierExtensions,
    title={Constrained Deep Networks: Lagrangian Optimization via Log-Barrier Extensions},
    author={Hoel Kervadec and Jose Dolz and Jing Yuan and Christian Desrosiers and Eric Granger and Ismail Ben Ayed},
    year={2019},
    eprint={1904.04205},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

@book{boyd__2004, 
place={Cambridge}, 
title={Convex Optimization}, 
DOI={10.1017/CBO9780511804441},
publisher={Cambridge University Press}, 
author={Boyd, Stephen and Vandenberghe, Lieven}, 
year={2004}}

@article{Chouzenoux2019,
  TITLE = {{A Proximal Interior Point Algorithm with Applications to Image Processing}},
  AUTHOR = {Chouzenoux, Emilie and Corbineau, Marie-Caroline and Pesquet, Jean-Christophe},
  URL = {https://hal.archives-ouvertes.fr/hal-02120005},
  JOURNAL = {{Journal of Mathematical Imaging and Vision}},
  PUBLISHER = {{Springer Verlag}},
  YEAR = {2019},
  KEYWORDS = {Interior point methods ; Armijo strategy ; Proximity operator ; proximity op- erator ; constrained optimization ; forward-backward algorithm ; variable metric ; line search ; hyperspectral unmixing ; geometry-texture decomposition},
  PDF = {https://hal.archives-ouvertes.fr/hal-02120005/file/JMIV_paper.pdf},
  HAL_ID = {hal-02120005},
  HAL_VERSION = {v2},
}
@ARTICLE{ACNN,
author={O. {Oktay} and E. {Ferrante} and K. {Kamnitsas} and M. {Heinrich} and W. {Bai} and J. {Caballero} and S. A. {Cook} and A. {de Marvao} and T. {Dawes} and D. P. {O‘Regan} and B. {Kainz} and B. {Glocker} and D. {Rueckert}}, 
journal={IEEE Transactions on Medical Imaging}, 
title={Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation}, 
year={2018}, 
volume={37}, 
number={2}, 
pages={384-395}, 
keywords={biomedical MRI;cardiology;image enhancement;image segmentation;learning (artificial intelligence);medical image processing;neural nets;cardiac pathologies;3-D shapes;learnt deep models;multimodal cardiac data sets;state-of-the-art models;different analysis tasks;learnt nonlinear representations;global anatomical properties;trained end-to-end;regularisation model;anatomical prior knowledge;generic training strategy;pixel-wise classifiers;state-of-the-art methods;recent promising techniques;anatomical objects;image acquisition;image analysis approaches;organ shape;incorporation;cardiac image enhancement;ACNNs;anatomically constrained neural networks;Image segmentation;Shape;Biomedical imaging;Computational modeling;Image resolution;Artificial neural networks;Motion segmentation;Shape prior;convolutional neural network;medical image segmentation;image super-resolution;Algorithms;Cardiac Imaging Techniques;Cardiomyopathies;Databases, Factual;Heart;Humans;Imaging, Three-Dimensional;Magnetic Resonance Imaging;Neural Networks (Computer)}, 
doi={10.1109/TMI.2017.2743464}, 
ISSN={0278-0062}, 
month={Feb},}




@inproceedings{zhang_gait_2019,
	title = {Gait {Recognition} via {Disentangled} {Representation} {Learning}},
	url = {http://openaccess.thecvf.com/content_CVPR_2019/html/Zhang_Gait_Recognition_via_Disentangled_Representation_Learning_CVPR_2019_paper.html},
	urldate = {2020-02-12},
	author = {Zhang, Ziyuan and Tran, Luan and Yin, Xi and Atoum, Yousef and Liu, Xiaoming and Wan, Jian and Wang, Nanxin},
	year = {2019},
	pages = {4710--4719}
}

@inproceedings{yang_unsupervised_2019,
	address = {Cham},
	series = {Lecture {Notes} in {Computer} {Science}},
	title = {Unsupervised {Domain} {Adaptation} via {Disentangled} {Representations}: {Application} to {Cross}-{Modality} {Liver} {Segmentation}},
	isbn = {978-3-030-32245-8},
	shorttitle = {Unsupervised {Domain} {Adaptation} via {Disentangled} {Representations}},
	doi = {10.1007/978-3-030-32245-8_29},
	abstract = {A deep learning model trained on some labeled data from a certain source domain generally performs poorly on data from different target domains due to domain shifts. Unsupervised domain adaptation methods address this problem by alleviating the domain shift between the labeled source data and the unlabeled target data. In this work, we achieve cross-modality domain adaptation, i.e. between CT and MRI images, via disentangled representations. Compared to learning a one-to-one mapping as the state-of-art CycleGAN, our model recovers a many-to-many mapping between domains to capture the complex cross-domain relations. It preserves semantic feature-level information by finding a shared content space instead of a direct pixelwise style transfer. Domain adaptation is achieved in two steps. First, images from each domain are embedded into two spaces, a shared domain-invariant content space and a domain-specific style space. Next, the representation in the content space is extracted to perform a task. We validated our method on a cross-modality liver segmentation task, to train a liver segmentation model on CT images that also performs well on MRI. Our method achieved Dice Similarity Coefficient (DSC) of 0.81, outperforming a CycleGAN-based method of 0.72. Moreover, our model achieved good generalization to joint-domain learning, in which unpaired data from different modalities are jointly learned to improve the segmentation performance on each individual modality. Lastly, under a multi-modal target domain with significant diversity, our approach exhibited the potential for diverse image generation and remained effective with DSC of 0.74 on multi-phasic MRI while the CycleGAN-based method performed poorly with a DSC of only 0.52.},
	language = {en},
	booktitle = {Medical {Image} {Computing} and {Computer} {Assisted} {Intervention} – {MICCAI} 2019},
	publisher = {Springer International Publishing},
	author = {Yang, Junlin and Dvornek, Nicha C. and Zhang, Fan and Chapiro, Julius and Lin, MingDe and Duncan, James S.},
	editor = {Shen, Dinggang and Liu, Tianming and Peters, Terry M. and Staib, Lawrence H. and Essert, Caroline and Zhou, Sean and Yap, Pew-Thian and Khan, Ali},
	year = {2019},
	pages = {255--263}
}

@article{chartsias_disentangled_2019,
	title = {Disentangled representation learning in cardiac image analysis},
	volume = {58},
	issn = {1361-8415},
	url = {http://www.sciencedirect.com/science/article/pii/S1361841519300684},
	doi = {10.1016/j.media.2019.101535},
	abstract = {Typically, a medical image offers spatial information on the anatomy (and pathology) modulated by imaging specific characteristics. Many imaging modalities including Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) can be interpreted in this way. We can venture further and consider that a medical image naturally factors into some spatial factors depicting anatomy and factors that denote the imaging characteristics. Here, we explicitly learn this decomposed (disentangled) representation of imaging data, focusing in particular on cardiac images. We propose Spatial Decomposition Network (SDNet), which factorises 2D medical images into spatial anatomical factors and non-spatial modality factors. We demonstrate that this high-level representation is ideally suited for several medical image analysis tasks, such as semi-supervised segmentation, multi-task segmentation and regression, and image-to-image synthesis. Specifically, we show that our model can match the performance of fully supervised segmentation models, using only a fraction of the labelled images. Critically, we show that our factorised representation also benefits from supervision obtained either when we use auxiliary tasks to train the model in a multi-task setting (e.g. regressing to known cardiac indices), or when aggregating multimodal data from different sources (e.g. pooling together MRI and CT data). To explore the properties of the learned factorisation, we perform latent-space arithmetic and show that we can synthesise CT from MR and vice versa, by swapping the modality factors. We also demonstrate that the factor holding image specific information can be used to predict the input modality with high accuracy. Code will be made available at https://github.com/agis85/anatomy\_modality\_decomposition.},
	language = {en},
	urldate = {2020-02-12},
	journal = {Medical Image Analysis},
	author = {Chartsias, Agisilaos and Joyce, Thomas and Papanastasiou, Giorgos and Semple, Scott and Williams, Michelle and Newby, David E. and Dharmakumar, Rohan and Tsaftaris, Sotirios A.},
	month = dec,
	year = {2019},
	keywords = {Cardiac magnetic resonance imaging, Disentangled representation learning, Multitask learning, Semi-supervised segmentation},
	pages = {101535}
}

@article{noauthor_assessing_nodate,
	title = {Assessing the validity of saliency maps for abnormality localization in medical imaging},
	url = {https://openreview.net/pdf?id=02X3kfP6W4}
}

@article{bresson_variational_2006,
	title = {A {Variational} {Model} for {Object} {Segmentation} {Using} {Boundary} {Information} and {Shape} {Prior} {Driven} by the {Mumford}-{Shah} {Functional}},
	volume = {68},
	issn = {1573-1405},
	url = {https://doi.org/10.1007/s11263-006-6658-x},
	doi = {10.1007/s11263-006-6658-x},
	abstract = {In this paper, we propose a new variational model to segment an object belonging to a given shape space using the active contour method, a geometric shape prior and the Mumford-Shah functional. The core of our model is an energy functional composed by three complementary terms. The first one is based on a shape model which constrains the active contour to get a shape of interest. The second term detects object boundaries from image gradients. And the third term drives globally the shape prior and the active contour towards a homogeneous intensity region. The segmentation of the object of interest is given by the minimum of our energy functional. This minimum is computed with the calculus of variations and the gradient descent method that provide a system of evolution equations solved with the well-known level set method. We also prove the existence of this minimum in the space of functions with bounded variation. Applications of the proposed model are presented on synthetic and medical images.},
	language = {en},
	number = {2},
	urldate = {2020-02-12},
	journal = {International Journal of Computer Vision},
	author = {Bresson, Xavier and Vandergheynst, Pierre and Thiran, Jean-Philippe},
	month = jun,
	year = {2006},
	keywords = {Mumford-Shah model, active contour, image segmentation, level set method, principal components analysis, shape prior, shape registration, variational model},
	pages = {145--162}
}

@misc{noauthor_unsupervised_nodate,
	title = {Unsupervised {Microvascular} {Image} {Segmentation} {Using} an {Active} {Contours} {Mimicking} {Neural} {Network}},
	url = {https://arxiv.org/abs/1908.01373},
	urldate = {2019-11-07}
}
@article{yue_cardiac_2019,
	title = {Cardiac {Segmentation} from {LGE} {MRI} {Using} {Deep} {Neural} {Network} {Incorporating} {Shape} and {Spatial} {Priors}},
	url = {http://arxiv.org/abs/1906.07347},
	abstract = {Cardiac segmentation from late gadolinium enhancement MRI is an important task in clinics to identify and evaluate the infarction of myocardium. The automatic segmentation is however still challenging, due to the heterogeneous intensity distributions and indistinct boundaries in the images. In this paper, we propose a new method, based on deep neural networks (DNN), for fully automatic segmentation. The proposed network, referred to as SRSCN, comprises a shape reconstruction neural network (SRNN) and a spatial constraint network (SCN). SRNN aims to maintain a realistic shape of the resulting segmentation. It can be pre-trained by a set of label images, and then be embedded into a unified loss function as a regularization term. Hence, no manually designed feature is needed. Furthermore, SCN incorporates the spatial information of the 2D slices. It is formulated and trained with the segmentation network via the multi-task learning strategy. We evaluated the proposed method using 45 patients and compared with two state-of-the-art regularization schemes, i.e., the anatomically constraint neural network and the adversarial neural network. The results show that the proposed SRSCN outperformed the conventional schemes, and obtained a Dice score of 0.758(std=0.227) for myocardial segmentation, which compares with 0.757(std=0.083) from the inter-observer variations.},
	urldate = {2020-02-12},
	journal = {arXiv:1906.07347 [cs, eess]},
	author = {Yue, Qian and Luo, Xinzhe and Ye, Qing and Xu, Lingchao and Zhuang, Xiahai},
	month = jun,
	year = {2019},
	note = {arXiv: 1906.07347},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing}
}

@article{minaee_image_2020,
	title = {Image {Segmentation} {Using} {Deep} {Learning}: {A} {Survey}},
	shorttitle = {Image {Segmentation} {Using} {Deep} {Learning}},
	url = {http://arxiv.org/abs/2001.05566},
	abstract = {Image segmentation is a key topic in image processing and computer vision with applications such as scene understanding, medical image analysis, robotic perception, video surveillance, augmented reality, and image compression, among many others. Various algorithms for image segmentation have been developed in the literature. Recently, due to the success of deep learning models in a wide range of vision applications, there has been a substantial amount of works aimed at developing image segmentation approaches using deep learning models. In this survey, we provide a comprehensive review of the literature at the time of this writing, covering a broad spectrum of pioneering works for semantic and instance-level segmentation, including fully convolutional pixel-labeling networks, encoder-decoder architectures, multi-scale and pyramid based approaches, recurrent networks, visual attention models, and generative models in adversarial settings. We investigate the similarity, strengths and challenges of these deep learning models, examine the most widely used datasets, report performances, and discuss promising future research directions in this area.},
	urldate = {2020-02-03},
	journal = {arXiv:2001.05566 [cs]},
	author = {Minaee, Shervin and Boykov, Yuri and Porikli, Fatih and Plaza, Antonio and Kehtarnavaz, Nasser and Terzopoulos, Demetri},
	month = jan,
	year = {2020},
	note = {arXiv: 2001.05566},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning}
}

@misc{noauthor_fast_nodate,
	title = {Fast {Global} {Minimization} of the {Active} {Contour}/{Snake} {Model} {\textbar} {SpringerLink}},
	url = {https://link.springer.com/article/10.1007/s10851-007-0002-0},
	urldate = {2020-01-31}
}

@incollection{cid-sueiro_proper_2012,
	title = {Proper losses for learning from partial labels},
	url = {http://papers.nips.cc/paper/4789-proper-losses-for-learning-from-partial-labels.pdf},
	urldate = {2020-01-28},
	booktitle = {Advances in {Neural} {Information} {Processing} {Systems} 25},
	publisher = {Curran Associates, Inc.},
	author = {Cid-sueiro, Jesús},
	editor = {Pereira, F. and Burges, C. J. C. and Bottou, L. and Weinberger, K. Q.},
	year = {2012},
	pages = {1565--1573}
}

@article{marquez-neila_imposing_2017,
	title = {Imposing {Hard} {Constraints} on {Deep} {Networks}: {Promises} and {Limitations}},
	shorttitle = {Imposing {Hard} {Constraints} on {Deep} {Networks}},
	url = {http://arxiv.org/abs/1706.02025},
	abstract = {Imposing constraints on the output of a Deep Neural Net is one way to improve the quality of its predictions while loosening the requirements for labeled training data. Such constraints are usually imposed as soft constraints by adding new terms to the loss function that is minimized during training. An alternative is to impose them as hard constraints, which has a number of theoretical benefits but has not been explored so far due to the perceived intractability of the problem. In this paper, we show that imposing hard constraints can in fact be done in a computationally feasible way and delivers reasonable results. However, the theoretical benefits do not materialize and the resulting technique is no better than existing ones relying on soft constraints. We analyze the reasons for this and hope to spur other researchers into proposing better solutions.},
	urldate = {2020-01-28},
	journal = {arXiv:1706.02025 [cs]},
	author = {Márquez-Neila, Pablo and Salzmann, Mathieu and Fua, Pascal},
	month = jun,
	year = {2017},
	note = {arXiv: 1706.02025},
	keywords = {Computer Science - Computer Vision and Pattern Recognition}
}

@article{qi_upi-net_nodate,
	title = {{UPI}-{Net}: {Semantic} {Contour} {Detection} in {Placental} {Ultrasound}},
	abstract = {Semantic contour detection is a challenging problem that is often met in medical imaging, of which placental image analysis is a particular example. In this paper, we investigate utero-placental interface (UPI) detection in 2D placental ultrasound images by formulating it as a semantic contour detection problem. As opposed to natural images, placental ultrasound images contain speciﬁc anatomical structures thus have unique geometry. We argue it would be beneﬁcial for UPI detectors to incorporate global context modelling in order to reduce unwanted false positive UPI predictions. Our approach, namely UPI-Net, aims to capture long-range dependencies in placenta geometry through lightweight global context modelling and effective multi-scale feature aggregation. We perform a subject-level 10-fold nested cross-validation on a placental ultrasound database (4,871 images with labelled UPI from 49 scans). Experimental results demonstrate that, without introducing considerable computational overhead, UPI-Net yields the highest performance in terms of standard contour detection metrics, compared to other competitive benchmarks.},
	language = {en},
	author = {Qi, Huan and Collins, Sally and Noble, J Alison},
	pages = {9}
}

@article{yao_land_2019,
	title = {Land {Use} {Classification} of the {Deep} {Convolutional} {Neural} {Network} {Method} {Reducing} the {Loss} of {Spatial} {Features}},
	volume = {19},
	copyright = {http://creativecommons.org/licenses/by/3.0/},
	url = {https://www.mdpi.com/1424-8220/19/12/2792},
	doi = {10.3390/s19122792},
	abstract = {Land use classification is a fundamental task of information extraction from remote sensing imagery. Semantic segmentation based on deep convolutional neural networks (DCNNs) has shown outstanding performance in this task. However, these methods are still affected by the loss of spatial features. In this study, we proposed a new network, called the dense-coordconv network (DCCN), to reduce the loss of spatial features and strengthen object boundaries. In this network, the coordconv module is introduced into the improved DenseNet architecture to improve spatial information by putting coordinate information into feature maps. The proposed DCCN achieved an obvious performance in terms of the public ISPRS (International Society for Photogrammetry and Remote Sensing) 2D semantic labeling benchmark dataset. Compared with the results of other deep convolutional neural networks (U-net, SegNet, Deeplab-V3), the results of the DCCN method improved a lot and the OA (overall accuracy) and mean F1 score reached 89.48\% and 86.89\%, respectively. This indicates that the DCCN method can effectively reduce the loss of spatial features and improve the accuracy of semantic segmentation in high resolution remote sensing imagery.},
	language = {en},
	number = {12},
	urldate = {2020-01-25},
	journal = {Sensors},
	author = {Yao, Xuedong and Yang, Hui and Wu, Yanlan and Wu, Penghai and Wang, Biao and Zhou, Xinxin and Wang, Shuai},
	month = jan,
	year = {2019},
	keywords = {DCNNs, ISPRS, coordconv, high resolution, remote sensing, semantic segmentation},
	pages = {2792}
}

@article{de_la_torre_weighted_2018,
	series = {Machine {Learning} and {Applications} in {Artificial} {Intelligence}},
	title = {Weighted kappa loss function for multi-class classification of ordinal data in deep learning},
	volume = {105},
	issn = {0167-8655},
	url = {http://www.sciencedirect.com/science/article/pii/S0167865517301666},
	doi = {10.1016/j.patrec.2017.05.018},
	abstract = {Weighted Kappa is an index of reference used in many diagnosis systems to compare the agreement between different raters. This index can be also used to evaluate the performance of automatic classification methods against the gold standard given by an expert (or from a consensus of an expert group). On the other hand, in the last years, deep learning has achieved a great importance as a new machine learning method. The usual loss function used in deep learning for multi-class classification is the logarithmic loss. In this paper we explore the direct use of a weighted kappa loss function for multi-class classification of ordinal data, also known as ordinal regression. Three classification problems are solved in the paper using these two loss functions. Results confirm that better classification is made when the model is constructed with the optimization of kappa instead of logarithmic loss.},
	language = {en},
	urldate = {2020-01-16},
	journal = {Pattern Recognition Letters},
	author = {de la Torre, Jordi and Puig, Domenec and Valls, Aida},
	month = apr,
	year = {2018},
	keywords = {Computer vision, Convolutional neural networks, Deep learning, Diabetic retinopathy, Supervised learning, Weighted kappa},
	pages = {144--154}
}

@misc{noauthor_190404205_nodate,
	title = {[1904.04205] {Constrained} {Deep} {Networks}: {Lagrangian} {Optimization} via {Log}-{Barrier} {Extensions}},
	url = {https://arxiv.org/abs/1904.04205},
	urldate = {2020-01-10}
}

@misc{noauthor_[1905.12868]_nodate,
	title = {[1905.12868] {Conditional} {Generative} {Adversarial} {Networks} for {Regression}},
	url = {https://arxiv.org/abs/1905.12868},
	urldate = {2019-12-17}
}

@misc{noauthor_[1504.00702]_nodate,
	title = {[1504.00702] {End}-to-{End} {Training} of {Deep} {Visuomotor} {Policies}},
	url = {https://arxiv.org/abs/1504.00702},
	urldate = {2019-12-17}
}

@article{jaderberg_spatial_2016,
	title = {Spatial {Transformer} {Networks}},
	url = {http://arxiv.org/abs/1506.02025},
	abstract = {Convolutional Neural Networks define an exceptionally powerful class of models, but are still limited by the lack of ability to be spatially invariant to the input data in a computationally and parameter efficient manner. In this work we introduce a new learnable module, the Spatial Transformer, which explicitly allows the spatial manipulation of data within the network. This differentiable module can be inserted into existing convolutional architectures, giving neural networks the ability to actively spatially transform feature maps, conditional on the feature map itself, without any extra training supervision or modification to the optimisation process. We show that the use of spatial transformers results in models which learn invariance to translation, scale, rotation and more generic warping, resulting in state-of-the-art performance on several benchmarks, and for a number of classes of transformations.},
	urldate = {2019-12-17},
	journal = {arXiv:1506.02025 [cs]},
	author = {Jaderberg, Max and Simonyan, Karen and Zisserman, Andrew and Kavukcuoglu, Koray},
	month = feb,
	year = {2016},
	note = {arXiv: 1506.02025},
	keywords = {Computer Science - Computer Vision and Pattern Recognition}
}

@article{lathuiliere_comprehensive_2019,
	title = {A {Comprehensive} {Analysis} of {Deep} {Regression}},
	volume = {41},
	url = {https://hal.inria.fr/hal-01754839},
	doi = {10.1109/TPAMI.2019.2910523},
	abstract = {Deep learning revolutionized data science, and recently its popularity has grown exponentially, as did the amount of papers employing deep networks. Vision tasks, such as human pose estimation, did not escape from this trend. There is a large number of deep models, where small changes in the network architecture, or in the data pre-processing, together with the stochastic nature of the optimization procedures, produce notably different results, making extremely difficult to sift methods that significantly outperform others. This situation motivates the current study, in which we perform a systematic evaluation and statistical analysis of vanilla deep regression, i.e. convolutional neural networks with a linear regression top layer. This is the first comprehensive analysis of deep regression techniques. We perform experiments on four vision problems, and report confidence intervals for the median performance as well as the statistical significance of the results, if any. Surprisingly, the variability due to different data pre-processing procedures generally eclipses the variability due to modifications in the network architecture. Our results reinforce the hypothesis according to which, in general, a general-purpose network (e.g. VGG-16 or ResNet-50) adequately tuned can yield results close to the state-of-the-art without having to resort to more complex and ad-hoc regression models.},
	language = {en},
	urldate = {2019-12-16},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	author = {Lathuilière, Stéphane and Mesejo, Pablo and Alameda-Pineda, Xavier and Horaud, Radu},
	month = apr,
	year = {2019},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
	pages = {1--17}
}

@misc{noauthor_[1506.02025]_nodate,
	title = {[1506.02025] {Spatial} {Transformer} {Networks}},
	url = {https://arxiv.org/abs/1506.02025},
	urldate = {2019-12-17}
}

@article{sinclair_cascaded_2018,
	title = {Cascaded {Transforming} {Multi}-task {Networks} {For} {Abdominal} {Biometric} {Estimation} from {Ultrasound}},
	url = {https://openreview.net/forum?id=r1ZGQW2if},
	abstract = {Measurement of biometrics from fetal ultrasound (US) images is of key importance in monitoring healthy fetal development. Under the time-constraints of a clinical setting however, accurate...},
	urldate = {2019-12-17},
	author = {Sinclair, Matthew D. and Martinez, Juan Cerrolaza and Skelton, Emily and Li, Yuanwei and Baumgartner, Christian F. and Bai, Wenjia and Matthew, Jacqueline and Knight, Caroline L. and Smith, Sandra and Hajnal, Jo and King, Andrew P. and Kainz, Bernhard and Rueckert, Daniel},
	month = apr,
	year = {2018}
}

@article{dubost_hydranet:_2019,
	title = {Hydranet: {Data} {Augmentation} for {Regression} {Neural} {Networks}},
	shorttitle = {Hydranet},
	url = {http://arxiv.org/abs/1807.04798},
	abstract = {Deep learning techniques are often criticized to heavily depend on a large quantity of labeled data. This problem is even more challenging in medical image analysis where the annotator expertise is often scarce. We propose a novel data-augmentation method to regularize neural network regressors that learn from a single global label per image. The principle of the method is to create new samples by recombining existing ones. We demonstrate the performance of our algorithm on two tasks: estimation of the number of enlarged perivascular spaces in the basal ganglia, and estimation of white matter hyperintensities volume. We show that the proposed method improves the performance over more basic data augmentation. The proposed method reached an intraclass correlation coefficient between ground truth and network predictions of 0.73 on the first task and 0.84 on the second task, only using between 25 and 30 scans with a single global label per scan for training. With the same number of training scans, more conventional data augmentation methods could only reach intraclass correlation coefficients of 0.68 on the first task, and 0.79 on the second task.},
	urldate = {2019-12-16},
	journal = {MICCAI 2019},
	author = {Dubost, Florian and Bortsova, Gerda and Adams, Hieab and Ikram, M. Arfan and Niessen, Wiro and Vernooij, Meike and de Bruijne, Marleen},
	month = jul,
	year = {2019},
	note = {arXiv: 1807.04798},
	keywords = {Computer Science - Computer Vision and Pattern Recognition}
}

@article{heuvel_automated_2018,
	title = {Automated measurement of fetal head circumference using {2D} ultrasound images},
	volume = {13},
	issn = {1932-6203},
	url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0200412},
	doi = {10.1371/journal.pone.0200412},
	abstract = {In this paper we present a computer aided detection (CAD) system for automated measurement of the fetal head circumference (HC) in 2D ultrasound images for all trimesters of the pregnancy. The HC can be used to estimate the gestational age and monitor growth of the fetus. Automated HC assessment could be valuable in developing countries, where there is a severe shortage of trained sonographers. The CAD system consists of two steps: First, Haar-like features were computed from the ultrasound images to train a random forest classifier to locate the fetal skull. Secondly, the HC was extracted using Hough transform, dynamic programming and an ellipse fit. The CAD system was trained on 999 images and validated on an independent test set of 335 images from all trimesters. The test set was manually annotated by an experienced sonographer and a medical researcher. The reference gestational age (GA) was estimated using the crown-rump length measurement (CRL). The mean difference between the reference GA and the GA estimated by the experienced sonographer was 0.8 ± 2.6, −0.0 ± 4.6 and 1.9 ± 11.0 days for the first, second and third trimester, respectively. The mean difference between the reference GA and the GA estimated by the medical researcher was 1.6 ± 2.7, 2.0 ± 4.8 and 3.9 ± 13.7 days. The mean difference between the reference GA and the GA estimated by the CAD system was 0.6 ± 4.3, 0.4 ± 4.7 and 2.5 ± 12.4 days. The results show that the CAD system performs comparable to an experienced sonographer. The presented system shows similar or superior results compared to systems published in literature. This is the first automated system for HC assessment evaluated on a large test set which contained data of all trimesters of the pregnancy.},
	language = {en},
	number = {8},
	urldate = {2019-12-16},
	journal = {PLOS ONE},
	author = {Heuvel, Thomas L. A. van den and Bruijn, Dagmar de and Korte, Chris L. de and Ginneken, Bram van},
	year = {2018},
	keywords = {Algorithms, Ellipses, Head, Imaging techniques, Medicine and health sciences, Pregnancy, Skull, Ultrasound imaging},
	pages = {e0200412}
}

@misc{t.l.a._van_den_heuvel_automated_2018,
	title = {Automated measurement of fetal head circumference},
	url = {https://zenodo.org/record/1322001#.Xff3edZKj6A},
	abstract = {For more information about this dataset go to: https://hc18.grand-challenge.org/},
	urldate = {2019-12-16},
	publisher = {Zenodo},
	author = {T.L.A. van den Heuvel and Dagmar de Bruijn and Chris L. de Korte and Bram van Ginneken},
	month = jul,
	year = {2018},
	doi = {10.5281/zenodo.1322001},
	note = {type: dataset}
}

@article{sobhaninia_fetal_2019,
	title = {Fetal {Ultrasound} {Image} {Segmentation} for {Measuring} {Biometric} {Parameters} {Using} {Multi}-{Task} {Deep} {Learning}},
	url = {http://arxiv.org/abs/1909.00273},
	abstract = {Ultrasound imaging is a standard examination during pregnancy that can be used for measuring specific biometric parameters towards prenatal diagnosis and estimating gestational age. Fetal head circumference (HC) is one of the significant factors to determine the fetus growth and health. In this paper, a multi-task deep convolutional neural network is proposed for automatic segmentation and estimation of HC ellipse by minimizing a compound cost function composed of segmentation dice score and MSE of ellipse parameters. Experimental results on fetus ultrasound dataset in different trimesters of pregnancy show that the segmentation results and the extracted HC match well with the radiologist annotations. The obtained dice scores of the fetal head segmentation and the accuracy of HC evaluations are comparable to the state-of-the-art.},
	urldate = {2019-12-16},
	journal = {arXiv:1909.00273 [cs, eess]},
	author = {Sobhaninia, Zahra and Rafiei, Shima and Emami, Ali and Karimi, Nader and Najarian, Kayvan and Samavi, Shadrokh and Soroushmehr, S. M. Reza},
	month = aug,
	year = {2019},
	note = {arXiv: 1909.00273},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing}
}

@incollection{ferrari_regularized_2018,
	address = {Cham},
	title = {On {Regularized} {Losses} for {Weakly}-supervised {CNN} {Segmentation}},
	volume = {11220},
	isbn = {978-3-030-01269-4 978-3-030-01270-0},
	url = {http://link.springer.com/10.1007/978-3-030-01270-0_31},
	abstract = {Minimization of regularized losses is a principled approach to weak supervision well-established in deep learning, in general. However, it is largely overlooked in semantic segmentation currently dominated by methods mimicking full supervision via “fake” fully-labeled masks (proposals) generated from available partial input. To obtain such full masks the typical methods explicitly use standard regularization techniques for “shallow” segmentation, e.g. graph cuts or dense CRFs. In contrast, we integrate such standard regularizers directly into the loss functions over partial input. This approach simpliﬁes weakly-supervised training by avoiding extra MRF/CRF inference steps or layers explicitly generating full masks, while improving both the quality and eﬃciency of training. This paper proposes and experimentally compares diﬀerent losses integrating MRF/CRF regularization terms. We juxtapose our regularized losses with earlier proposal-generation methods. Our approach achieves state-of-the-art accuracy in semantic segmentation with near full-supervision quality.},
	language = {en},
	urldate = {2019-12-16},
	booktitle = {Computer {Vision} – {ECCV} 2018},
	publisher = {Springer International Publishing},
	author = {Tang, Meng and Perazzi, Federico and Djelouah, Abdelaziz and Ayed, Ismail Ben and Schroers, Christopher and Boykov, Yuri},
	editor = {Ferrari, Vittorio and Hebert, Martial and Sminchisescu, Cristian and Weiss, Yair},
	year = {2018},
	doi = {10.1007/978-3-030-01270-0_31},
	pages = {524--540}
}

@inproceedings{kanezaki_unsupervised_2018,
	address = {Calgary, AB},
	title = {Unsupervised {Image} {Segmentation} by {Backpropagation}},
	isbn = {978-1-5386-4658-8},
	url = {https://ieeexplore.ieee.org/document/8462533/},
	doi = {10.1109/ICASSP.2018.8462533},
	abstract = {We investigate the use of convolutional neural networks (CNNs) for unsupervised image segmentation. As in the case of supervised image segmentation, the proposed CNN assigns labels to pixels that denote the cluster to which the pixel belongs. In the unsupervised scenario, however, no training images or ground truth labels of pixels are given beforehand. Therefore, once when a target image is input, we jointly optimize the pixel labels together with feature representations while their parameters are updated by gradient descent. In the proposed approach, we alternately iterate label prediction and network parameter learning to meet the following criteria: (a) pixels of similar features are desired to be assigned the same label, (b) spatially continuous pixels are desired to be assigned the same label, and (c) the number of unique labels is desired to be large. Although these criteria are incompatible, the proposed approach ﬁnds a plausible solution of label assignment that balances well the above criteria, which demonstrates good performance on a benchmark dataset of image segmentation.},
	language = {en},
	urldate = {2019-12-16},
	booktitle = {2018 {IEEE} {International} {Conference} on {Acoustics}, {Speech} and {Signal} {Processing} ({ICASSP})},
	publisher = {IEEE},
	author = {Kanezaki, Asako},
	month = apr,
	year = {2018},
	pages = {1543--1547}
}

@article{chen_learning_nodate,
	title = {Learning {Active} {Contour} {Models} for {Medical} {Image} {Segmentation}},
	abstract = {Image segmentation is an important step in medical image processing and has been widely studied and developed for reﬁnement of clinical analysis and applications. New models based on deep learning have improved results but are restricted to pixel-wise tting of the segmentation map. Our aim was to tackle this limitation by developing a new model based on deep learning which takes into account the area inside as well as outside the region of interest as well as the size of boundaries during learning. Specically, we propose a new loss function which incorporates area and size information and integrates this into a dense deep learning model. We evaluated our approach on a dataset of more than 2,000 cardiac MRI scans. Our results show that the proposed loss function outperforms other mainstream loss function Cross-entropy on two common segmentation networks. Our loss function is robust while using different hyperparameter λ.},
	language = {en},
	author = {Chen, Xu and Williams, Bryan M and Vallabhaneni, Srinivasa R and Czanner, Gabriela and Williams, Rachel and Zheng, Yalin},
	pages = {9}
}



@misc{noauthor_[1809.00076]_nodate,
	title = {[1809.00076] {3D} {Segmentation} with {Exponential} {Logarithmic} {Loss} for {Highly} {Unbalanced} {Object} {Sizes}},
	url = {https://arxiv.org/abs/1809.00076},
	urldate = {2019-12-11}
}



@article{xie_microscopy_nodate,
	title = {Microscopy {Cell} {Counting} with {Fully} {Convolutional} {Regression} {Networks}},
	abstract = {This paper concerns automated cell counting in microscopy images. The approach we take is to use Convolutional Neural Networks (CNNs) to regress a cell spatial density across the image. This is applicable to situations where traditional single-cell segmentation based methods do not work well due to cell clumping or overlap.},
	language = {en},
	author = {Xie, Weidi and Noble, J Alison and Zisserman, Andrew},
	pages = {8}
}

@article{dubost_3d_2018,
	title = {{3D} {Regression} {Neural} {Network} for the {Quantification} of {Enlarged} {Perivascular} {Spaces} in {Brain} {MRI}},
	url = {http://arxiv.org/abs/1802.05914},
	abstract = {Enlarged perivascular spaces (EPVS) in the brain are an emerging imaging marker for cerebral small vessel disease, and have been shown to be related to increased risk of various neurological diseases, including stroke and dementia. Automatic quantification of EPVS would greatly help to advance research into its etiology and its potential as a risk indicator of disease. We propose a convolutional network regression method to quantify the extent of EPVS in the basal ganglia from 3D brain MRI. We first segment the basal ganglia and subsequently apply a 3D convolutional regression network designed for small object detection within this region of interest. The network takes an image as input, and outputs a quantification score of EPVS. The network has significantly more convolution operations than pooling ones and no final activation, allowing it to span the space of real numbers. We validated our approach using a dataset of 2000 brain MRI scans scored visually. Experiments with varying sizes of training and test sets showed that a good performance can be achieved with a training set of only 200 scans. With a training set of 1000 scans, the intraclass correlation coefficient (ICC) between our scoring method and the expert's visual score was 0.74. Our method outperforms by a large margin - more than 0.10 - four more conventional automated approaches based on intensities, scale-invariant feature transform, and random forest. We show that the network learns the structures of interest and investigate the influence of hyper-parameters on the performance. We also evaluate the reproducibility of our network using a set of 60 subjects scanned twice (scan-rescan reproducibility). On this set our network achieves an ICC of 0.93, while the intrarater agreement reaches 0.80. Furthermore, the automatic EPVS scoring correlates similarly to age as visual scoring.},
	urldate = {2019-12-10},
	journal = {arXiv:1802.05914 [cs]},
	author = {Dubost, Florian and Adams, Hieab and Bortsova, Gerda and Ikram, M. Arfan and Niessen, Wiro and Vernooij, Meike and de Bruijne, Marleen},
	month = oct,
	year = {2018},
	note = {arXiv: 1802.05914},
	keywords = {Computer Science - Computer Vision and Pattern Recognition}
}

@Article{trullo17,
author = {Roger Trullo and Caroline Petitjean and Su Ruan and  Bernard Dubray and Dong Nie and Dinggang Shen},
title = {Joint Segmentation of Multiple Thoracic Organs in {CT} Images with Two Collaborative Deep Architectures},
journal = {{MICCAI'17 workshop Deep Learning in Medical Image Analysis}},
year = {2017}
}

@Article{trullo18jmi,
  author = {Roger Trullo and C. Petitjean  and Su Ruan and	Bernard Dubray},
  title = {Multi-Organ Segmentation using Distance Aware Adversarial Networks},
  journal = {{Journal of Medical Imaging}},
   volume = {6},
  number = {1},
  pages = {014001},
  doi = {10.1117/1.JMI.6.1.014001},
  year = {2019}
}

@Article{grosgeorge16,
author = {D. Grosgeorge and C. Petitjean and Su Ruan},
title = {Multilabel statistical shape prior for image segmentation },
journal = {{IET Image Processing}},
year = {2016},
volume = {10},
number = {10},
pages = {710--716}
}


@article{kim_mumford-shah_2019,
	title = {Mumford-{Shah} {Loss} {Functional} for {Image} {Segmentation} with {Deep} {Learning}},
	url = {http://arxiv.org/abs/1904.02872},
	abstract = {Recent state-of-the-art image segmentation algorithms are mostly based on deep neural networks, thanks to their high performance and fast computation time. However, these methods are usually trained in a supervised manner, which requires large number of high quality ground-truth segmentation masks. On the other hand, classical image segmentation approaches such as level-set methods are formulated in a self-supervised manner by minimizing energy functions such as Mumford-Shah functional, so they are still useful to help generation of segmentation masks without labels. Unfortunately, these algorithms are usually computationally expensive and often have limitation in semantic segmentation. In this paper, we propose a novel loss function based on Mumford-Shah functional that can be used in deep-learning based image segmentation without or with small labeled data. This loss function is based on the observation that the softmax layer of deep neural networks has striking similarity to the characteristic function in the Mumford-Shah functional. We show that the new loss function enables semi-supervised and unsupervised segmentation. In addition, our loss function can be also used as a regularized function to enhance supervised semantic segmentation algorithms. Experimental results on multiple datasets demonstrate the effectiveness of the proposed method.},
	urldate = {2019-12-10},
	journal = {arXiv:1904.02872 [cs, stat]},
	author = {Kim, Boah and Ye, Jong Chul},
	month = sep,
	year = {2019},
	note = {arXiv: 1904.02872},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning}
}

@inproceedings{bertels_optimizing_2019,
	address = {Cham},
	series = {Lecture {Notes} in {Computer} {Science}},
	title = {Optimizing the {Dice} {Score} and {Jaccard} {Index} for {Medical} {Image} {Segmentation}: {Theory} and {Practice}},
	isbn = {978-3-030-32245-8},
	shorttitle = {Optimizing the {Dice} {Score} and {Jaccard} {Index} for {Medical} {Image} {Segmentation}},
	doi = {10.1007/978-3-030-32245-8_11},
	abstract = {The Dice score and Jaccard index are commonly used metrics for the evaluation of segmentation tasks in medical imaging. Convolutional neural networks trained for image segmentation tasks are usually optimized for (weighted) cross-entropy. This introduces an adverse discrepancy between the learning optimization objective (the loss) and the end target metric. Recent works in computer vision have proposed soft surrogates to alleviate this discrepancy and directly optimize the desired metric, either through relaxations (soft-Dice, soft-Jaccard) or submodular optimization (Lovász-softmax). The aim of this study is two-fold. First, we investigate the theoretical differences in a risk minimization framework and question the existence of a weighted cross-entropy loss with weights theoretically optimized to surrogate Dice or Jaccard. Second, we empirically investigate the behavior of the aforementioned loss functions w.r.t. evaluation with Dice score and Jaccard index on five medical segmentation tasks. Through the application of relative approximation bounds, we show that all surrogates are equivalent up to a multiplicative factor, and that no optimal weighting of cross-entropy exists to approximate Dice or Jaccard measures. We validate these findings empirically and show that, while it is important to opt for one of the target metric surrogates rather than a cross-entropy-based loss, the choice of the surrogate does not make a statistical difference on a wide range of medical segmentation tasks.},
	language = {en},
	booktitle = {Medical {Image} {Computing} and {Computer} {Assisted} {Intervention} – {MICCAI} 2019},
	publisher = {Springer International Publishing},
	author = {Bertels, Jeroen and Eelbode, Tom and Berman, Maxim and Vandermeulen, Dirk and Maes, Frederik and Bisschops, Raf and Blaschko, Matthew B.},
	editor = {Shen, Dinggang and Liu, Tianming and Peters, Terry M. and Staib, Lawrence H. and Essert, Caroline and Zhou, Sean and Yap, Pew-Thian and Khan, Ali},
	year = {2019},
	keywords = {Cross-entropy, Dice, Jaccard, Risk minimization},
	pages = {92--100}
}

@article{liu_intriguing_2018,
	title = {An {Intriguing} {Failing} of {Convolutional} {Neural} {Networks} and the {CoordConv} {Solution}},
	url = {http://arxiv.org/abs/1807.03247},
	abstract = {Few ideas have enjoyed as large an impact on deep learning as convolution. For any problem involving pixels or spatial representations, common intuition holds that convolutional neural networks may be appropriate. In this paper we show a striking counterexample to this intuition via the seemingly trivial coordinate transform problem, which simply requires learning a mapping between coordinates in (x, y) Cartesian space and coordinates in one-hot pixel space. Although convolutional networks would seem appropriate for this task, we show that they fail spectacularly. We demonstrate and carefully analyze the failure ﬁrst on a toy problem, at which point a simple ﬁx becomes obvious. We call this solution CoordConv, which works by giving convolution access to its own input coordinates through the use of extra coordinate channels. Without sacriﬁcing the computational and parametric efﬁciency of ordinary convolution, CoordConv allows networks to learn either complete translation invariance or varying degrees of translation dependence, as required by the end task. CoordConv solves the coordinate transform problem with perfect generalization and 150 times faster with 10–100 times fewer parameters than convolution. This stark contrast raises the question: to what extent has this inability of convolution persisted insidiously inside other tasks, subtly hampering performance from within? A complete answer to this question will require further investigation, but we show preliminary evidence that swapping convolution for CoordConv can improve models on a diverse set of tasks. Using CoordConv in a GAN produced less mode collapse as the transform between high-level spatial latents and pixels becomes easier to learn. A Faster R-CNN detection model trained on MNIST detection showed 24\% better IOU when using CoordConv, and in the Reinforcement Learning (RL) domain agents playing Atari games beneﬁt signiﬁcantly from the use of CoordConv layers.},
	language = {en},
	urldate = {2019-12-04},
	journal = {arXiv:1807.03247 [cs, stat]},
	author = {Liu, Rosanne and Lehman, Joel and Molino, Piero and Such, Felipe Petroski and Frank, Eric and Sergeev, Alex and Yosinski, Jason},
	month = dec,
	year = {2018},
	note = {arXiv: 1807.03247},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning}
}

@article{mortazi_weakly_2019,
	title = {Weakly {Supervised} {Segmentation} by {A} {Deep} {Geodesic} {Prior}},
	url = {http://arxiv.org/abs/1908.06498},
	abstract = {The performance of the state-of-the-art image segmentation methods heavily relies on the high-quality annotations, which are not easily affordable, particularly for medical data. To alleviate this limitation, in this study, we propose a weakly supervised image segmentation method based on a deep geodesic prior. We hypothesize that integration of this prior information can reduce the adverse effects of weak labels in segmentation accuracy. Our proposed algorithm is based on a prior information, extracted from an auto-encoder, trained to map objects geodesic maps to their corresponding binary maps. The obtained information is then used as an extra term in the loss function of the segmentor. In order to show efficacy of the proposed strategy, we have experimented segmentation of cardiac substructures with clean and two levels of noisy labels (L1, L2). Our experiments showed that the proposed algorithm boosted the performance of baseline deep learning-based segmentation for both clean and noisy labels by 4.4\%, 4.6\%(L1), and 6.3\%(L2) in dice score, respectively. We also showed that the proposed method was more robust in the presence of high-level noise due to the existence of shape priors.},
	urldate = {2019-12-03},
	journal = {arXiv:1908.06498 [cs, stat]},
	author = {Mortazi, Aliasghar and Khosravan, Naji and Torigian, Drew A. and Kurugol, Sila and Bagci, Ulas},
	month = aug,
	year = {2019},
	note = {arXiv: 1908.06498},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning}
}

@inproceedings{ICML-2019-SymmetricalLosses,
	author        = "Nontawat Charoenphakdee and Jongyeong Lee and Masashi Sugiyama",
	booktitle     = "{ICML, PMLR}",
	ee            = "http://proceedings.mlr.press/v97/charoenphakdee19a.html",
	pages         = "961--970",
	publisher     = "{PMLR}",
	title         = "{On Symmetric Losses for Learning from Corrupted Labels}",
	year          = 2019,
}


@article{lee_tetris:_2019,
	title = {{TeTrIS}: {Template} {Transformer} {Networks} for {Image} {Segmentation} {With} {Shape} {Priors}},
	volume = {38},
	issn = {1558-254X},
	shorttitle = {{TeTrIS}},
	doi = {10.1109/TMI.2019.2905990},
	abstract = {In this paper, we introduce and compare different approaches for incorporating shape prior information into neural network-based image segmentation. Specifically, we introduce the concept of template transformer networks, where a shape template is deformed to match the underlying structure of interest through an end-to-end trained spatial transformer network. This has the advantage of explicitly enforcing shape priors, and this is free of discretization artifacts by providing a soft partial volume segmentation. We also introduce a simple yet effective way of incorporating priors in the state-of-the-art pixel-wise binary classification methods such as fully convolutional networks and U-net. Here, the template shape is given as an additional input channel, incorporating this information significantly reduces false positives. We report results on synthetic data and sub-voxel segmentation of coronary lumen structures in cardiac computed tomography showing the benefit of incorporating priors in neural network-based image segmentation.},
	number = {11},
	journal = {IEEE Transactions on Medical Imaging},
	author = {Lee, Matthew Chung Hai and Petersen, Kersten and Pawlowski, Nick and Glocker, Ben and Schaap, Michiel},
	month = nov,
	year = {2019},
	keywords = {Deformable models, Image segmentation, Neural networks, Shape, Strain, Task analysis, Training, computerised tomography, end-to-end trained spatial transformer network, fully convolutional networks, image classification, image registration, image segmentation, learning (artificial intelligence), medical image processing, neural nets, neural network-based image segmentation, neural networks, shape prior information, shape priors, shape template, soft partial volume segmentation, state-of-the-art pixel-wise binary classification methods, sub-voxel segmentation, template deformation, template shape, template transformer networks},
	pages = {2596--2606}
}

@article{hoyer_grid_2019,
	title = {Grid {Saliency} for {Context} {Explanations} of {Semantic} {Segmentation}},
	url = {http://arxiv.org/abs/1907.13054},
	abstract = {Recently, there has been a growing interest in developing saliency methods that provide visual explanations of network predictions. Still, the usability of existing methods is limited to image classification models. To overcome this limitation, we extend the existing approaches to generate grid saliencies, which provide spatially coherent visual explanations for (pixel-level) dense prediction networks. As the proposed grid saliency allows to spatially disentangle the object and its context, we specifically explore its potential to produce context explanations for semantic segmentation networks, discovering which context most influences the class predictions inside a target object area. We investigate the effectiveness of grid saliency on a synthetic dataset with an artificially induced bias between objects and their context as well as on the real-world Cityscapes dataset using state-of-the-art segmentation networks. Our results show that grid saliency can be successfully used to provide easily interpretable context explanations and, moreover, can be employed for detecting and localizing contextual biases present in the data.},
	urldate = {2019-11-24},
	journal = {arXiv:1907.13054 [cs]},
	author = {Hoyer, Lukas and Munoz, Mauricio and Katiyar, Prateek and Khoreva, Anna and Fischer, Volker},
	month = nov,
	year = {2019},
	note = {arXiv: 1907.13054},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning}
}

@article{patra_saliency_nodate,
	title = {Saliency aware continual learning in medical image analysis},
	abstract = {While medical image analysis has seen extensive use of deep neural networks, learning over multiple tasks is a challenge for connectionist networks due to tendencies of degradation in performance over old tasks while adapting to novel tasks. It is pertinent that adaptations to new data distributions over time are tractable with automated analysis methods as medical imaging data acquisition is typically not a static problem. So, one needs to ensure that a continual learning paradigm be ensured in machine learning methods deployed for medical imaging. To explore interpretable lifelong learning for deep neural networks in medical imaging, we introduce a perspective of understanding forgetting in neural networks through the notions of attention and saliency. Concretely, we propose quantiﬁcation of forgetting as a decline in the quality of class speciﬁc saliency maps after each subsequent task schedule. We also introduce a knowledge transfer from past tasks to present by a saliency guided retention of past exemplars which help augment the ability to retain past knowledge while optimising parameters for current tasks. Experiments on a clinical fetal echocardiography dataset demonstrate a state-of-the-art performance for our protocols.},
	language = {en},
	author = {Patra, Arijit and Noble, J Alison},
	pages = {13}
}


@article{bach_pixel-wise_2015,
	title = {On {Pixel}-{Wise} {Explanations} for {Non}-{Linear} {Classifier} {Decisions} by {Layer}-{Wise} {Relevance} {Propagation}},
	volume = {10},
	issn = {1932-6203},
	url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0130140},
	doi = {10.1371/journal.pone.0130140},
	abstract = {Understanding and interpreting classification decisions of automated image classification systems is of high value in many applications, as it allows to verify the reasoning of the system and provides additional information to the human expert. Although machine learning methods are solving very successfully a plethora of tasks, they have in most cases the disadvantage of acting as a black box, not providing any information about what made them arrive at a particular decision. This work proposes a general solution to the problem of understanding classification decisions by pixel-wise decomposition of nonlinear classifiers. We introduce a methodology that allows to visualize the contributions of single pixels to predictions for kernel-based classifiers over Bag of Words features and for multilayered neural networks. These pixel contributions can be visualized as heatmaps and are provided to a human expert who can intuitively not only verify the validity of the classification decision, but also focus further analysis on regions of potential interest. We evaluate our method for classifiers trained on PASCAL VOC 2009 images, synthetic image data containing geometric shapes, the MNIST handwritten digits data set and for the pre-trained ImageNet model available as part of the Caffe open source package.},
	language = {en},
	number = {7},
	urldate = {2019-11-19},
	journal = {PLOS ONE},
	author = {Bach, Sebastian and Binder, Alexander and Montavon, Grégoire and Klauschen, Frederick and Müller, Klaus-Robert and Samek, Wojciech},
	year = {2015},
	keywords = {Algorithms, Coding mechanisms, Imaging techniques, Kernel functions, Kernel methods, Neural networks, Neurons, Vision},
	pages = {e0130140}
}
@inproceedings{rezatofighi_generalized_2019,
	title = {Generalized {Intersection} {Over} {Union}: {A} {Metric} and a {Loss} for {Bounding} {Box} {Regression}},
	shorttitle = {Generalized {Intersection} {Over} {Union}},
	url = {http://openaccess.thecvf.com/content_CVPR_2019/html/Rezatofighi_Generalized_Intersection_Over_Union_A_Metric_and_a_Loss_for_CVPR_2019_paper.html},
	urldate = {2019-11-13},
	author = {Rezatofighi, Hamid and Tsoi, Nathan and Gwak, JunYoung and Sadeghian, Amir and Reid, Ian and Savarese, Silvio},
	year = {2019},
	pages = {658--666}
}

@misc{noauthor_disentangling_nodate,
	title = {Disentangling {Factors} of {Variation} {Using} {Few} {Labels}},
	url = {https://arxiv.org/abs/1905.01258},
	urldate = {2019-10-28}
}

@article{ke_multi-task_2019,
	title = {A multi-task {U}-net for segmentation with lazy labels},
	url = {http://arxiv.org/abs/1906.12177},
	abstract = {The need for labour intensive pixel-wise annotation is a major limitation of many fully supervised learning methods for image segmentation. In this paper, we propose a deep convolutional neural network for multi-class segmentation that circumvents this problem by being trainable on coarse data labels combined with only a very small number of images with pixel-wise annotations. We call this new labelling strategy 'lazy' labels. Image segmentation is then stratified into three connected tasks: rough detection of class instances, separation of wrongly connected objects without a clear boundary, and pixel-wise segmentation to find the accurate boundaries of each object. These problems are integrated into a multitask learning framework and the model is trained end-to-end in a semi-supervised fashion. The method is applied on a dataset of food microscopy images. We show that the model gives accurate segmentation results even if exact boundary labels are missing for a majority of the annotated data. This allows more flexibility and efficiency for training deep neural networks that are data hungry in a practical setting where manual annotation is expensive, by collecting more lazy (rough) annotations than precisely segmented images.},
	urldate = {2019-11-07},
	journal = {arXiv:1906.12177 [cs, stat]},
	author = {Ke, Rihuan and Bugeau, Aurélie and Papadakis, Nicolas and Schuetz, Peter and Schönlieb, Carola-Bibiane},
	month = jun,
	year = {2019},
	note = {arXiv: 1906.12177},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning}
}

@inproceedings{liu_ganredl:_2019,
	address = {Cham},
	series = {Lecture {Notes} in {Computer} {Science}},
	title = {{GANReDL}: {Medical} {Image} {Enhancement} {Using} a {Generative} {Adversarial} {Network} with {Real}-{Order} {Derivative} {Induced} {Loss} {Functions}},
	isbn = {978-3-030-32248-9},
	shorttitle = {{GANReDL}},
	doi = {10.1007/978-3-030-32248-9_13},
	abstract = {Deep (convolutional) neural networks (DCNN) have recently gained popularity, and shown improved performance in the field of image enhancement (de-noising and super-resolution, for instance). However, the central issue of recovering finer texture details in images still remains unsolved. State-of-the-art objective functions used in DCNN mostly focus on minimizing the mean squared reconstruction error. The resulting image estimates have high peak signal-to-noise ratios, but they are often lacking high-frequency details, and are therefore error-prone with respect to fine-scale, possibly clinically relevant details.In this article, we present GANReDL, a generative adversarial network (GAN) for image enhancement equipped with a real-order derivative induced loss functions (ReDL) which we will show gives improved images, in particular wrt to the reconstruction of fine-scale details. To the best of our knowledge, this is the first framework that incorporates non-integer order derivatives in loss functions. To this aim, we propose a discriminator network that is trained to differentiate between the enhanced images and ground-truth images, and propose a new loss function motivated by real-order derivatives which is capable of also capturing global image features rather than pixel-wise features only. We show, with several numerical experiments, that GANReDL is better in reconstructing the high-frequency image details, and therefore show improved performance for image enhancement over other state-of-the-art methods.},
	language = {en},
	booktitle = {Medical {Image} {Computing} and {Computer} {Assisted} {Intervention} – {MICCAI} 2019},
	publisher = {Springer International Publishing},
	author = {Liu, Pan and Li, Chao and Schönlieb, Carola-Bibiane},
	editor = {Shen, Dinggang and Liu, Tianming and Peters, Terry M. and Staib, Lawrence H. and Essert, Caroline and Zhou, Sean and Yap, Pew-Thian and Khan, Ali},
	year = {2019},
	keywords = {Generative adversarial networks, Medical image super-resolution, Real order derivative operators},
	pages = {110--117}
}

@article{chaitanya_semi-supervised_2019,
	title = {Semi-{Supervised} and {Task}-{Driven} {Data} {Augmentation}},
	url = {http://arxiv.org/abs/1902.05396},
	abstract = {Supervised deep learning methods for segmentation require large amounts of labelled training data, without which they are prone to overﬁtting, not generalizing well to unseen images. In practice, obtaining a large number of annotations from clinical experts is expensive and time-consuming. One way to address scarcity of annotated examples is data augmentation using random spatial and intensity transformations. Recently, it has been proposed to use generative models to synthesize realistic training examples, complementing the random augmentation. So far, these methods have yielded limited gains over the random augmentation. However, there is potential to improve the approach by (i) explicitly modeling deformation ﬁelds (non-aﬃne spatial transformation) and intensity transformations and (ii) leveraging unlabelled data during the generative process. With this motivation, we propose a novel task-driven data augmentation method where to synthesize new training examples, a generative network explicitly models and applies deformation ﬁelds and additive intensity masks on existing labelled data, modeling shape and intensity variations, respectively. Crucially, the generative model is optimized to be conducive to the task, in this case segmentation, and constrained to match the distribution of images observed from labelled and unlabelled samples. Furthermore, explicit modeling of deformation ﬁelds allow synthesizing segmentation masks and images in exact correspondence by simply applying the generated transformation to an input image and the corresponding annotation. Our experiments on cardiac magnetic resonance images (MRI) showed that, for the task of segmentation in small training data scenarios, the proposed method substantially outperforms conventional augmentation techniques.},
	language = {en},
	urldate = {2019-11-04},
	journal = {arXiv:1902.05396 [cs, stat]},
	author = {Chaitanya, Krishna and Karani, Neerav and Baumgartner, Christian and Donati, Olivio and Becker, Anton and Konukoglu, Ender},
	month = feb,
	year = {2019},
	note = {arXiv: 1902.05396},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning, veronika}
}

@article{taha_metrics_2015,
	title = {Metrics for evaluating {3D} medical image segmentation: analysis, selection, and tool},
	volume = {15},
	issn = {1471-2342},
	shorttitle = {Metrics for evaluating {3D} medical image segmentation},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4533825/},
	doi = {10.1186/s12880-015-0068-x},
	abstract = {Background
Medical Image segmentation is an important image processing step. Comparing images to evaluate the quality of segmentation is an essential part of measuring progress in this research area. Some of the challenges in evaluating medical segmentation are: metric selection, the use in the literature of multiple definitions for certain metrics, inefficiency of the metric calculation implementations leading to difficulties with large volumes, and lack of support for fuzzy segmentation by existing metrics.

Result
First we present an overview of 20 evaluation metrics selected based on a comprehensive literature review. For fuzzy segmentation, which shows the level of membership of each voxel to multiple classes, fuzzy definitions of all metrics are provided. We present a discussion about metric properties to provide a guide for selecting evaluation metrics. Finally, we propose an efficient evaluation tool implementing the 20 selected metrics. The tool is optimized to perform efficiently in terms of speed and required memory, also if the image size is extremely large as in the case of whole body MRI or CT volume segmentation. An implementation of this tool is available as an open source project.

Conclusion
We propose an efficient evaluation tool for 3D medical image segmentation using 20 evaluation metrics and provide guidelines for selecting a subset of these metrics that is suitable for the data and the segmentation task.},
	urldate = {2019-11-04},
	journal = {BMC Medical Imaging},
	author = {Taha, Abdel Aziz and Hanbury, Allan},
	month = aug,
	year = {2015},
	pmid = {26263899},
	pmcid = {PMC4533825}
}

@article{tajbakhsh_embracing_2019,
	title = {Embracing {Imperfect} {Datasets}: {A} {Review} of {Deep} {Learning} {Solutions} for {Medical} {Image} {Segmentation}},
	shorttitle = {Embracing {Imperfect} {Datasets}},
	url = {http://arxiv.org/abs/1908.10454},
	abstract = {The medical imaging literature has witnessed remarkable progress in high-performing segmentation models based on convolutional neural networks. Despite the new performance highs, the recent advanced segmentation models still require large, representative, and high quality annotated datasets. However, rarely do we have a perfect training dataset, particularly in the field of medical imaging, where data and annotations are both expensive to acquire. Recently, a large body of research has studied the problem of medical image segmentation with imperfect datasets, tackling two major dataset limitations: scarce annotations where only limited annotated data is available for training, and weak annotations where the training data has only sparse annotations, noisy annotations, or image-level annotations. In this article, we provide a detailed review of the solutions above, summarizing both the technical novelties and empirical results. We further compare the benefits and requirements of the surveyed methodologies and provide our recommended solutions to the problems of scarce and weak annotations. We hope this review increases the community awareness of the techniques to handle imperfect datasets.},
	urldate = {2019-11-04},
	journal = {arXiv:1908.10454 [cs, eess]},
	author = {Tajbakhsh, Nima and Jeyaseelan, Laura and Li, Qian and Chiang, Jeffrey and Wu, Zhihao and Ding, Xiaowei},
	month = aug,
	year = {2019},
	note = {arXiv: 1908.10454
version: 1},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing}
}
@ARTICLE{karimi_reducing_2019,
  author={Karimi, Davood and Salcudean, Septimiu E.},
  journal={IEEE Transactions on Medical Imaging}, 
  title={Reducing the Hausdorff Distance in Medical Image Segmentation With Convolutional Neural Networks}, 
  year={2020},
  volume={39},
  number={2},
  pages={499-513},
  doi={10.1109/TMI.2019.2930068}}


@article{bateson_constrained_2019,
	title = {Constrained domain adaptation for segmentation},
	url = {http://arxiv.org/abs/1908.02996},
	abstract = {We propose to adapt segmentation networks with a constrained formulation, which embeds domain-invariant prior knowledge about the segmentation regions. Such knowledge may take the form of simple anatomical information, e.g., structure size or shape, estimated from source samples or known a priori. Our method imposes domain-invariant inequality constraints on the network outputs of unlabeled target samples. It implicitly matches prediction statistics between target and source domains with permitted uncertainty of prior knowledge. We address our constrained problem with a differentiable penalty, fully suited for standard stochastic gradient descent approaches, removing the need for computationally expensive Lagrangian optimization with dual projections. Unlike current two-step adversarial training, our formulation is based on a single loss in a single network, which simplifies adaptation by avoiding extra adversarial steps, while improving convergence and quality of training. The comparison of our approach with state-of-the-art adversarial methods reveals substantially better performance on the challenging task of adapting spine segmentation across different MRI modalities. Our results also show a robustness to imprecision of size priors, approaching the accuracy of a fully supervised model trained directly in a target domain.Our method can be readily used for various constraints and segmentation problems.},
	urldate = {2019-10-27},
	journal = {arXiv:1908.02996 [cs]},
	author = {Bateson, Mathilde and Dolz, Jose and Kervadec, Hoel and Lombaert, Hervé and Ayed, Ismail Ben},
	month = aug,
	year = {2019},
	note = {arXiv: 1908.02996},
	keywords = {Computer Science - Computer Vision and Pattern Recognition}
}

@article{taghanaki_deep_2019,
	title = {Deep {Semantic} {Segmentation} of {Natural} and {Medical} {Images}: {A} {Review}},
	shorttitle = {Deep {Semantic} {Segmentation} of {Natural} and {Medical} {Images}},
	url = {http://arxiv.org/abs/1910.07655},
	abstract = {The (medical) image semantic segmentation task consists of classifying each pixel of an image (or just several ones) into an instance, where each instance (or category) corresponding to a class. This task is a part of the concept of scene understanding or better explaining the global context of an image. In the medical image analysis domain, image segmentation can be used for image-guided interventions, radiotherapy, or improved radiological diagnostics. In this review, we categorize the main deep learning-based medical and non-medical image segmentation solutions into six main groups of deep architectural improvements, data synthesis-based, loss function-based improvements, sequenced models, weakly supervised, and multi-task methods and further for each group we analyzed each variant of these groups and discuss limitations of the current approaches and future research directions for semantic image segmentation.},
	urldate = {2019-10-27},
	journal = {arXiv:1910.07655 [cs, eess]},
	author = {Taghanaki, Saed Asgari and Abhishek, Kumar and Cohen, Joseph Paul and Cohen-Adad, Julien and Hamarneh, Ghassan},
	month = oct,
	year = {2019},
	note = {arXiv: 1910.07655},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing}
}

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@misc{kervadecgithub,
  author = {Hoel Kervadec},
  title = {Constrained-{CNN} losses for weakly supervised segmentation},
  year = {2019},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/LIVIAETS/SizeLoss_WSS}},
  commit = {4f57d6a0e4c030202a07a60bc1bb1ed1544bf679}
}

@article{lambert2019segthor,
title={SegTHOR: Segmentation of Thoracic Organs at Risk in {CT} images},
author={Z. Lambert and C. Petitjean and B. Dubray and S. Ruan},
  journal   = {CoRR},
  volume    = {abs/1912.05950},
  year      = {2019},
  
}

@article{ACDC,
  title = {Deep {{Learning Techniques}} for {{Automatic MRI Cardiac Multi}}-{{Structures Segmentation}} and {{Diagnosis}}: {{Is}} the {{Problem Solved}}?},
  shorttitle = {Deep {{Learning Techniques}} for {{Automatic MRI Cardiac Multi}}-{{Structures Segmentation}} and {{Diagnosis}}},
  author = {Bernard, Olivier and Lalande, Alain and Zotti, Clement and Cervenansky, Frederick and Yang, Xin and Heng, Pheng-Ann and Cetin, Irem and Lekadir, Karim and Camara, Oscar and Gonzalez Ballester, Miguel Angel and Sanroma, Gerard and Napel, Sandy and Petersen, Steffen and Tziritas, Georgios and Grinias, Elias and Khened, Mahendra and Kollerathu, Varghese Alex and Krishnamurthi, Ganapathy and Rohe, Marc-Michel and Pennec, Xavier and Sermesant, Maxime and Isensee, Fabian and Jager, Paul and Maier-Hein, Klaus H. and Full, Peter M. and Wolf, Ivo and Engelhardt, Sandy and Baumgartner, Christian F. and Koch, Lisa M. and Wolterink, Jelmer M. and Isgum, Ivana and Jang, Yeonggul and Hong, Yoonmi and Patravali, Jay and Jain, Shubham and Humbert, Olivier and Jodoin, Pierre-Marc},
  date = {2018-11},
  journaltitle = {IEEE Transactions on Medical Imaging},
  shortjournal = {IEEE Trans. Med. Imaging},
  volume = {37},
  pages = {2514--2525},
  issn = {0278-0062, 1558-254X},
  doi = {10.1109/TMI.2018.2837502},
  url = {https://ieeexplore.ieee.org/document/8360453/},
  urldate = {2021-04-21},
  file = {/home/eljurros/Zotero/storage/6XK79LVU/Bernard et al. - 2018 - Deep Learning Techniques for Automatic MRI Cardiac.pdf},
  langid = {english},
  number = {11}
}


@article{E-NET,
  title={ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation},
  author={Adam Paszke and Abhishek Chaurasia and Sangpil Kim and Eugenio Culurciello},
  journal={ArXiv},
  year={2016},
  volume={abs/1606.02147}
}

@InProceedings{Wang2018,
author="Wang, Guotai
and Li, Wenqi
and Ourselin, S{\'e}bastien
and Vercauteren, Tom",
title="Automatic Brain Tumor Segmentation Using Cascaded Anisotropic Convolutional Neural Networks",
booktitle="Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries",
year="2018",
pages="178--190",
abstract="A cascade of fully convolutional neural networks is proposed to segment multi-modal Magnetic Resonance (MR) images with brain tumor into background and three hierarchical regions: whole tumor, tumor core and enhancing tumor core. The cascade is designed to decompose the multi-class segmentation problem into a sequence of three binary segmentation problems according to the subregion hierarchy. The whole tumor is segmented in the first step and the bounding box of the result is used for the tumor core segmentation in the second step. The enhancing tumor core is then segmented based on the bounding box of the tumor core segmentation result. Our networks consist of multiple layers of anisotropic and dilated convolution filters, and they are combined with multi-view fusion to reduce false positives. Residual connections and multi-scale predictions are employed in these networks to boost the segmentation performance. Experiments with BraTS 2017 validation set show that the proposed method achieved average Dice scores of 0.7859, 0.9050, 0.8378 for enhancing tumor core, whole tumor and tumor core, respectively. The corresponding values for BraTS 2017 testing set were 0.7831, 0.8739, and 0.7748, respectively.",
isbn="978-3-319-75238-9"
}








% url       = {http://arxiv.org/abs/1606.02147},
%   archivePrefix = {arXiv},
%   eprint    = {1606.02147},
%   timestamp = {Mon, 13 Aug 2018 16:47:37 +0200},
%   biburl    = {https://dblp.org/rec/bib/journals/corr/PaszkeCKC16},
%   bibsource = {dblp computer science bibliography, https://dblp.org}
@article{Tran2016,
  author    = {Tran Minh Quan and
               David G. C. Hildebrand and
               Won{-}Ki Jeong},
  title     = {FusionNet: {A} deep fully residual convolutional neural network for
               image segmentation in connectomics},
  journal   = {CoRR},
  volume    = {abs/1612.05360},
  year      = {2016},
  
  archivePrefix = {arXiv},
  eprint    = {1612.05360},
  timestamp = {Mon, 13 Aug 2018 16:46:57 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/QuanHJ16},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

% url ={http://arxiv.org/abs/1612.05360},
@article{Bischof2019,
title = "Integrating spatial configuration into heatmap regression based {CNN}s for landmark localization",
abstract = "In many medical image analysis applications, only a limited amount of training data is available due to the costs of image acquisition and the large manual annotation effort required from experts. Training recent state-of-the-art machine learning methods like convolutional neural networks ({{CNN}}s) from small datasets is a challenging task. In this work on anatomical landmark localization, we propose a {{CNN}} architecture that learns to split the localization task into two simpler sub-problems, reducing the overall need for large training datasets. Our fully convolutional SpatialConfiguration-Net (SCN) learns this simplification due to multiplying the heatmap predictions of its two components and by training the network in an end-to-end manner. Thus, the SCN dedicates one component to locally accurate but ambiguous candidate predictions, while the other component improves robustness to ambiguities by incorporating the …",
author = "Horst Bischof and Darko Stern and Christian Payer and Martin Urschler",
year = "2019",
language = "deutsch",
pages = "207--219",
journal = "Medical image analysis",
issn = "1361-8415",
publisher = "Elsevier B.V.",
}

@article{Ghafoorian2016,
author = {Ghafoorian, Mohsen and Karssemeijer, Nico and Heskes, Tom and Van Uden, Inge and Sanchez, Clara and Litjens, Geert and Leeuw, Frank-Erik and Ginneken, Bram and Marchiori, Elena and Platel, Bram},
year = {2016},
month = {10},
pages = {},
title = {Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensities},
volume = {7},
journal = {Scientific Reports},
}
% doi = {10.1038/s41598-017-05300-5}


@inproceedings{qi_upi-net:_2019,
	title = {{UPI}-{Net}: {Semantic} {Contour} {Detection} in {Placental} {Ultrasound}},
	shorttitle = {{UPI}-{Net}},
	
	abstract = {Semantic contour detection is a challenging problem that is often met in medical imaging, of which placental image analysis is a particular example. In this paper, we investigate utero-placental interface (UPI) detection in 2D placental ultrasound images by formulating it as a semantic contour detection problem. As opposed to natural images, placental ultrasound images contain specific anatomical structures thus have unique geometry. We argue it would be beneficial for UPI detectors to incorporate global context modelling in order to reduce unwanted false positive UPI predictions. Our approach, namely UPI-Net, aims to capture long-range dependencies in placenta geometry through lightweight global context modelling and effective multi-scale feature aggregation. We perform a subject-level 10-fold nested cross-validation on a placental ultrasound database (4,871 images with labelled UPI from 49 scans). Experimental results demonstrate that, without introducing considerable computational overhead, UPI-Net yields the highest performance in terms of standard contour detection metrics, compared to other competitive benchmarks.},
	urldate = {2020-01-25},
	booktitle = {Visual {Recognition} for {Medical} {Images} ({VRMI}), {ICCV} 2019 workshop},
	author = {Qi, Huan and Collins, Sally and Noble, J. Alison},
	month = sep,
	year = {2019},
	note = {arXiv: 1909.00229},
	keywords = {Computer Science - Computer Vision and Pattern Recognition}
}
% url = {http://arxiv.org/abs/1909.00229},

@article{yao_land_2019,
	title = {Land {Use} {Classification} of the {Deep} {Convolutional} {Neural} {Network} {Method} {Reducing} the {Loss} of {Spatial} {Features}},
	volume = {19},
	abstract = {Land use classification is a fundamental task of information extraction from remote sensing imagery. Semantic segmentation based on deep convolutional neural networks (D{{CNN}}s) has shown outstanding performance in this task. However, these methods are still affected by the loss of spatial features. In this study, we proposed a new network, called the dense-coordconv network (DCCN), to reduce the loss of spatial features and strengthen object boundaries. In this network, the coordconv module is introduced into the improved DenseNet architecture to improve spatial information by putting coordinate information into feature maps. The proposed DCCN achieved an obvious performance in terms of the public ISPRS (International Society for Photogrammetry and Remote Sensing) 2D semantic labeling benchmark dataset. Compared with the results of other deep convolutional neural networks (U-net, SegNet, Deeplab-V3), the results of the DCCN method improved a lot and the OA (overall accuracy) and mean F1 score reached 89.48\% and 86.89\%, respectively. This indicates that the DCCN method can effectively reduce the loss of spatial features and improve the accuracy of semantic segmentation in high resolution remote sensing imagery.},
	language = {en},
	number = {12},
	urldate = {2020-01-25},
	journal = {Sensors},
	author = {Yao, Xuedong and Yang, Hui and Wu, Yanlan and Wu, Penghai and Wang, Biao and Zhou, Xinxin and Wang, Shuai},
	month = jan,
	year = {2019},
	keywords = {D{{CNN}}s, ISPRS, coordconv, high resolution, remote sensing, semantic segmentation},
	pages = {2792}
}

@article{de_la_torre_weighted_2018,
	series = {Machine {Learning} and {Applications} in {Artificial} {Intelligence}},
	title = {Weighted kappa loss function for multi-class classification of ordinal data in deep learning},
	volume = {105},
	issn = {0167-8655},
	url = {http://www.sciencedirect.com/science/article/pii/S0167865517301666},
	doi = {10.1016/j.patrec.2017.05.018},
	abstract = {Weighted Kappa is an index of reference used in many diagnosis systems to compare the agreement between different raters. This index can be also used to evaluate the performance of automatic classification methods against the gold standard given by an expert (or from a consensus of an expert group). On the other hand, in the last years, deep learning has achieved a great importance as a new machine learning method. The usual loss function used in deep learning for multi-class classification is the logarithmic loss. In this paper we explore the direct use of a weighted kappa loss function for multi-class classification of ordinal data, also known as ordinal regression. Three classification problems are solved in the paper using these two loss functions. Results confirm that better classification is made when the model is constructed with the optimization of kappa instead of logarithmic loss.},
	language = {en},
	urldate = {2020-01-16},
	journal = {Pattern Recognition Letters},
	author = {de la Torre, Jordi and Puig, Domenec and Valls, Aida},
	month = apr,
	year = {2018},
	keywords = {Computer vision, Convolutional neural networks, Deep learning, Diabetic retinopathy, Supervised learning, Weighted kappa},
	pages = {144--154}
}

@misc{noauthor_[1904.04205]_nodate,
	title = {[1904.04205] {Constrained} {Deep} {Networks}: {Lagrangian} {Optimization} via {Log}-{Barrier} {Extensions}},
	url = {https://arxiv.org/abs/1904.04205},
	urldate = {2020-01-10}
}

@article{storath_fast_2014,
	title = {Fast {Partitioning} of {Vector}-{Valued} {Images}},
	volume = {7},
	doi = {10.1137/130950367},
	abstract = {We propose a fast splitting approach to the classical variational formulation of the image partitioning problem, which is frequently referred to as the Potts or piecewise constant Mumford--Shah model. For vector-valued images, our approach is significantly faster than the methods based on graph cuts and convex relaxations of the Potts model which are presently the state-of-the-art. The computational costs of our algorithm only grow linearly with the dimension of the data space which contrasts the exponential growth of the state-of-the-art methods. This allows us to process images with high-dimensional codomains such as multispectral images. Our approach produces results of a quality comparable with that of graph cuts and the convex relaxation strategies, and we do not need an a priori discretization of the label space. Furthermore, the number of partitions has almost no influence on the computational costs, which makes our algorithm also suitable for the reconstruction of piecewise constant (color or vect...},
	journal = {SIAM J. Imaging Sciences},
	author = {Storath, Martin and Weinmann, Andreas},
	year = {2014},
	keywords = {Algorithm, Calculus of variations, Color, Computation, Cut (graph theory), Dataspaces, Discretization, Linear programming relaxation, Multispectral image, Partition problem, Potts model, Time complexity},
	pages = {1826--1852}
}

@misc{noauthor_[1905.12868]_nodate,
	title = {[1905.12868] {Conditional} {Generative} {Adversarial} {Networks} for {Regression}},
	url = {https://arxiv.org/abs/1905.12868},
	urldate = {2019-12-17}
}

@article{levine_end--end_2016,
	title = {End-to-end {Training} of {Deep} {Visuomotor} {Policies}},
	volume = {17},
	issn = {1532-4435},
	url = {http://dl.acm.org/citation.cfm?id=2946645.2946684},
	abstract = {Policy search methods can allow robots to learn control policies for a wide range of tasks, but practical applications of policy search often require hand-engineered components for perception, state estimation, and low-level control. In this paper, we aim to answer the following question: does training the perception and control systems jointly end-to-end provide better performance than training each component separately? To this end, we develop a method that can be used to learn policies that map raw image observations directly to torques at the robot's motors. The policies are represented by deep convolutional neural networks ({{CNN}}s) with 92,000 parameters, and are trained using a guided policy search method, which transforms policy search into supervised learning, with supervision provided by a simple trajectory-centric reinforcement learning method. We evaluate our method on a range of real-world manipulation tasks that require close coordination between vision and control, such as screwing a cap onto a bottle, and present simulated comparisons to a range of prior policy search methods.},
	number = {1},
	urldate = {2019-12-17},
	journal = {J. Mach. Learn. Res.},
	author = {Levine, Sergey and Finn, Chelsea and Darrell, Trevor and Abbeel, Pieter},
	month = jan,
	year = {2016},
	keywords = {neural networks, optimal control, reinforcement learning, vision},
	pages = {1334--1373}
}

@article{levine_end--end_nodate,
	title = {End-to-{End} {Training} of {Deep} {Visuomotor} {Policies}},
	abstract = {Policy search methods can allow robots to learn control policies for a wide range of tasks, but practical applications of policy search often require hand-engineered components for perception, state estimation, and low-level control. In this paper, we aim to answer the following question: does training the perception and control systems jointly end-toend provide better performance than training each component separately? To this end, we develop a method that can be used to learn policies that map raw image observations directly to torques at the robot’s motors. The policies are represented by deep convolutional neural networks ({{CNN}}s) with 92,000 parameters, and are trained using a guided policy search method, which transforms policy search into supervised learning, with supervision provided by a simple trajectory-centric reinforcement learning method. We evaluate our method on a range of real-world manipulation tasks that require close coordination between vision and control, such as screwing a cap onto a bottle, and present simulated comparisons to a range of prior policy search methods.},
	language = {en},
	author = {Levine, Sergey and Finn, Chelsea and Darrell, Trevor and Abbeel, Pieter},
	pages = {40}
}

@misc{noauthor_[1504.00702]_nodate,
	title = {[1504.00702] {End}-to-{End} {Training} of {Deep} {Visuomotor} {Policies}},
	url = {https://arxiv.org/abs/1504.00702},
	urldate = {2019-12-17}
}

@article{jaderberg_spatial_2016,
	title = {Spatial {Transformer} {Networks}},
	url = {http://arxiv.org/abs/1506.02025},
	abstract = {Convolutional Neural Networks define an exceptionally powerful class of models, but are still limited by the lack of ability to be spatially invariant to the input data in a computationally and parameter efficient manner. In this work we introduce a new learnable module, the Spatial Transformer, which explicitly allows the spatial manipulation of data within the network. This differentiable module can be inserted into existing convolutional architectures, giving neural networks the ability to actively spatially transform feature maps, conditional on the feature map itself, without any extra training supervision or modification to the optimisation process. We show that the use of spatial transformers results in models which learn invariance to translation, scale, rotation and more generic warping, resulting in state-of-the-art performance on several benchmarks, and for a number of classes of transformations.},
	urldate = {2019-12-17},
	journal = {arXiv:1506.02025 [cs]},
	author = {Jaderberg, Max and Simonyan, Karen and Zisserman, Andrew and Kavukcuoglu, Koray},
	month = feb,
	year = {2016},
	note = {arXiv: 1506.02025},
	keywords = {Computer Science - Computer Vision and Pattern Recognition}
}

@article{lathuiliere_comprehensive_2019,
	title = {A {Comprehensive} {Analysis} of {Deep} {Regression}},
	volume = {41},
	url = {https://hal.inria.fr/hal-01754839},
	doi = {10.1109/TPAMI.2019.2910523},
	abstract = {Deep learning revolutionized data science, and recently its popularity has grown exponentially, as did the amount of papers employing deep networks. Vision tasks, such as human pose estimation, did not escape from this trend. There is a large number of deep models, where small changes in the network architecture, or in the data pre-processing, together with the stochastic nature of the optimization procedures, produce notably different results, making extremely difficult to sift methods that significantly outperform others. This situation motivates the current study, in which we perform a systematic evaluation and statistical analysis of vanilla deep regression, i.e. convolutional neural networks with a linear regression top layer. This is the first comprehensive analysis of deep regression techniques. We perform experiments on four vision problems, and report confidence intervals for the median performance as well as the statistical significance of the results, if any. Surprisingly, the variability due to different data pre-processing procedures generally eclipses the variability due to modifications in the network architecture. Our results reinforce the hypothesis according to which, in general, a general-purpose network (e.g. VGG-16 or ResNet-50) adequately tuned can yield results close to the state-of-the-art without having to resort to more complex and ad-hoc regression models.},
	language = {en},
	urldate = {2019-12-16},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	author = {Lathuilière, Stéphane and Mesejo, Pablo and Alameda-Pineda, Xavier and Horaud, Radu},
	month = apr,
	year = {2019},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
	pages = {1--17}
}

@misc{noauthor_[1506.02025]_nodate,
	title = {[1506.02025] {Spatial} {Transformer} {Networks}},
	url = {https://arxiv.org/abs/1506.02025},
	urldate = {2019-12-17}
}

@article{sinclair_cascaded_2018,
	title = {Cascaded {Transforming} {Multi}-task {Networks} {For} {Abdominal} {Biometric} {Estimation} from {Ultrasound}},
	url = {https://openreview.net/forum?id=r1ZGQW2if},
	abstract = {Measurement of biometrics from fetal ultrasound (US) images is of key importance in monitoring healthy fetal development. Under the time-constraints of a clinical setting however, accurate...},
	urldate = {2019-12-17},
	author = {Sinclair, Matthew D. and Martinez, Juan Cerrolaza and Skelton, Emily and Li, Yuanwei and Baumgartner, Christian F. and Bai, Wenjia and Matthew, Jacqueline and Knight, Caroline L. and Smith, Sandra and Hajnal, Jo and King, Andrew P. and Kainz, Bernhard and Rueckert, Daniel},
	month = apr,
	year = {2018}
}

@article{dubost_hydranet:_2019,
	title = {Hydranet: {Data} {Augmentation} for {Regression} {Neural} {Networks}},
	shorttitle = {Hydranet},
	url = {http://arxiv.org/abs/1807.04798},
	abstract = {Deep learning techniques are often criticized to heavily depend on a large quantity of labeled data. This problem is even more challenging in medical image analysis where the annotator expertise is often scarce. We propose a novel data-augmentation method to regularize neural network regressors that learn from a single global label per image. The principle of the method is to create new samples by recombining existing ones. We demonstrate the performance of our algorithm on two tasks: estimation of the number of enlarged perivascular spaces in the basal ganglia, and estimation of white matter hyperintensities volume. We show that the proposed method improves the performance over more basic data augmentation. The proposed method reached an intraclass correlation coefficient between ground truth and network predictions of 0.73 on the first task and 0.84 on the second task, only using between 25 and 30 scans with a single global label per scan for training. With the same number of training scans, more conventional data augmentation methods could only reach intraclass correlation coefficients of 0.68 on the first task, and 0.79 on the second task.},
	urldate = {2019-12-16},
	journal = {MICCAI 2019},
	author = {Dubost, Florian and Bortsova, Gerda and Adams, Hieab and Ikram, M. Arfan and Niessen, Wiro and Vernooij, Meike and de Bruijne, Marleen},
	month = jul,
	year = {2019},
	note = {arXiv: 1807.04798},
	keywords = {Computer Science - Computer Vision and Pattern Recognition}
}

@article{heuvel_automated_2018,
	title = {Automated measurement of fetal head circumference using 2D ultrasound images},
	volume = {13},
	issn = {1932-6203},
	url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0200412},
	doi = {10.1371/journal.pone.0200412},
	abstract = {In this paper we present a computer aided detection (CAD) system for automated measurement of the fetal head circumference (HC) in 2D ultrasound images for all trimesters of the pregnancy. The HC can be used to estimate the gestational age and monitor growth of the fetus. Automated HC assessment could be valuable in developing countries, where there is a severe shortage of trained sonographers. The CAD system consists of two steps: First, Haar-like features were computed from the ultrasound images to train a random forest classifier to locate the fetal skull. Secondly, the HC was extracted using Hough transform, dynamic programming and an ellipse fit. The CAD system was trained on 999 images and validated on an independent test set of 335 images from all trimesters. The test set was manually annotated by an experienced sonographer and a medical researcher. The reference gestational age (GA) was estimated using the crown-rump length measurement (CRL). The mean difference between the reference GA and the GA estimated by the experienced sonographer was 0.8 ± 2.6, −0.0 ± 4.6 and 1.9 ± 11.0 days for the first, second and third trimester, respectively. The mean difference between the reference GA and the GA estimated by the medical researcher was 1.6 ± 2.7, 2.0 ± 4.8 and 3.9 ± 13.7 days. The mean difference between the reference GA and the GA estimated by the CAD system was 0.6 ± 4.3, 0.4 ± 4.7 and 2.5 ± 12.4 days. The results show that the CAD system performs comparable to an experienced sonographer. The presented system shows similar or superior results compared to systems published in literature. This is the first automated system for HC assessment evaluated on a large test set which contained data of all trimesters of the pregnancy.},
	language = {en},
	number = {8},
	urldate = {2019-12-16},
	journal = {PLOS ONE},
	author = {Heuvel, Thomas L. A. van den and Bruijn, Dagmar de and Korte, Chris L. de and Ginneken, Bram van},
	year = {2018},
	keywords = {Algorithms, Ellipses, Head, Imaging techniques, Medicine and health sciences, Pregnancy, Skull, Ultrasound imaging},
	pages = {e0200412}
}

@misc{t.l.a._van_den_heuvel_automated_2018,
	title = {Automated measurement of fetal head circumference},
	url = {https://zenodo.org/record/1322001#.Xff3edZKj6A},
	abstract = {For more information about this dataset go to: https://hc18.grand-challenge.org/},
	urldate = {2019-12-16},
	publisher = {Zenodo},
	author = {T.L.A. van den Heuvel and Dagmar de Bruijn and Chris L. de Korte and Bram van Ginneken},
	month = jul,
	year = {2018},
	doi = {10.5281/zenodo.1322001},
	note = {type: dataset}
}

@article{sobhaninia_fetal_2019,
	title = {Fetal {Ultrasound} {Image} {Segmentation} for {Measuring} {Biometric} {Parameters} {Using} {Multi}-{Task} {Deep} {Learning}},
	url = {http://arxiv.org/abs/1909.00273},
	abstract = {Ultrasound imaging is a standard examination during pregnancy that can be used for measuring specific biometric parameters towards prenatal diagnosis and estimating gestational age. Fetal head circumference (HC) is one of the significant factors to determine the fetus growth and health. In this paper, a multi-task deep convolutional neural network is proposed for automatic segmentation and estimation of HC ellipse by minimizing a compound cost function composed of segmentation dice score and MSE of ellipse parameters. Experimental results on fetus ultrasound dataset in different trimesters of pregnancy show that the segmentation results and the extracted HC match well with the radiologist annotations. The obtained dice scores of the fetal head segmentation and the accuracy of HC evaluations are comparable to the state-of-the-art.},
	urldate = {2019-12-16},
	journal = {arXiv:1909.00273 [cs, eess]},
	author = {Sobhaninia, Zahra and Rafiei, Shima and Emami, Ali and Karimi, Nader and Najarian, Kayvan and Samavi, Shadrokh and Soroushmehr, S. M. Reza},
	month = aug,
	year = {2019},
	note = {arXiv: 1909.00273},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing}
}

@incollection{ferrari_regularized_2018,
	address = {Cham},
	title = {On {Regularized} {Losses} for {Weakly}-supervised {{CNN}} {Segmentation}},
	volume = {11220},
	isbn = {978-3-030-01269-4 978-3-030-01270-0},
	url = {http://link.springer.com/10.1007/978-3-030-01270-0_31},
	abstract = {Minimization of regularized losses is a principled approach to weak supervision well-established in deep learning, in general. However, it is largely overlooked in semantic segmentation currently dominated by methods mimicking full supervision via “fake” fully-labeled masks (proposals) generated from available partial input. To obtain such full masks the typical methods explicitly use standard regularization techniques for “shallow” segmentation, e.g. graph cuts or dense CRFs. In contrast, we integrate such standard regularizers directly into the loss functions over partial input. This approach simpliﬁes weakly-supervised training by avoiding extra MRF/CRF inference steps or layers explicitly generating full masks, while improving both the quality and eﬃciency of training. This paper proposes and experimentally compares diﬀerent losses integrating MRF/CRF regularization terms. We juxtapose our regularized losses with earlier proposal-generation methods. Our approach achieves state-of-the-art accuracy in semantic segmentation with near full-supervision quality.},
	language = {en},
	urldate = {2019-12-16},
	booktitle = {Computer {Vision} – {ECCV} 2018},
	publisher = {Springer International Publishing},
	author = {Tang, Meng and Perazzi, Federico and Djelouah, Abdelaziz and Ayed, Ismail Ben and Schroers, Christopher and Boykov, Yuri},
	editor = {Ferrari, Vittorio and Hebert, Martial and Sminchisescu, Cristian and Weiss, Yair},
	year = {2018},
	doi = {10.1007/978-3-030-01270-0_31},
	pages = {524--540}
}

@inproceedings{kanezaki_unsupervised_2018,
	address = {Calgary, AB},
	title = {Unsupervised {Image} {Segmentation} by {Backpropagation}},
	isbn = {978-1-5386-4658-8},
	url = {https://ieeexplore.ieee.org/document/8462533/},
	doi = {10.1109/ICASSP.2018.8462533},
	abstract = {We investigate the use of convolutional neural networks ({CNN}s) for unsupervised image segmentation. As in the case of supervised image segmentation, the proposed {CNN} assigns labels to pixels that denote the cluster to which the pixel belongs. In the unsupervised scenario, however, no training images or ground truth labels of pixels are given beforehand. Therefore, once when a target image is input, we jointly optimize the pixel labels together with feature representations while their parameters are updated by gradient descent. In the proposed approach, we alternately iterate label prediction and network parameter learning to meet the following criteria: (a) pixels of similar features are desired to be assigned the same label, (b) spatially continuous pixels are desired to be assigned the same label, and (c) the number of unique labels is desired to be large. Although these criteria are incompatible, the proposed approach ﬁnds a plausible solution of label assignment that balances well the above criteria, which demonstrates good performance on a benchmark dataset of image segmentation.},
	language = {en},
	urldate = {2019-12-16},
	booktitle = {2018 {IEEE} {International} {Conference} on {Acoustics}, {Speech} and {Signal} {Processing} ({ICASSP})},
	publisher = {IEEE},
	author = {Kanezaki, Asako},
	month = apr,
	year = {2018},
	pages = {1543--1547}
}

@article{chen_learning_nodate,
	title = {Learning {Active} {Contour} {Models} for {Medical} {Image} {Segmentation}},
	abstract = {Image segmentation is an important step in medical image processing and has been widely studied and developed for reﬁnement of clinical analysis and applications. New models based on deep learning have improved results but are restricted to pixel-wise tting of the segmentation map. Our aim was to tackle this limitation by developing a new model based on deep learning which takes into account the area inside as well as outside the region of interest as well as the size of boundaries during learning. Specically, we propose a new loss function which incorporates area and size information and integrates this into a dense deep learning model. We evaluated our approach on a dataset of more than 2,000 cardiac MRI scans. Our results show that the proposed loss function outperforms other mainstream loss function Cross-entropy on two common segmentation networks. Our loss function is robust while using different hyperparameter λ.},
	language = {en},
	author = {Chen, Xu and Williams, Bryan M and Vallabhaneni, Srinivasa R and Czanner, Gabriela and Williams, Rachel and Zheng, Yalin},
	pages = {9}
}


@misc{noauthor_[1809.00076]_nodate,
	title = {[1809.00076] 3D {Segmentation} with {Exponential} {Logarithmic} {Loss} for {Highly} {Unbalanced} {Object} {Sizes}},
	url = {https://arxiv.org/abs/1809.00076},
	urldate = {2019-12-11}
}

@article{kervadec_constrained-cnn_2019,
  author    = {Hoel Kervadec and
               Jose Dolz and
               Meng Tang and
               Eric Granger and
               Yuri Boykov and
               Ismail Ben Ayed},
  title     = {Constrained-{CNN} losses for weakly supervised segmentation},
  journal   = {Medical Image Analysis},
  volume    = {54},
  pages     = {88--99},
  year      = {2019},
  
  
  timestamp = {Mon, 09 Dec 2019 13:47:06 +0100},
  biburl    = {https://dblp.org/rec/bib/journals/mia/KervadecDTGBA19},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
% url       = {https://doi.org/10.1016/j.media.2019.02.009},
% doi       = {10.1016/j.media.2019.02.009},
@article{xie_microscopy_nodate,
	title = {Microscopy {Cell} {Counting} with {Fully} {Convolutional} {Regression} {Networks}},
	abstract = {This paper concerns automated cell counting in microscopy images. The approach we take is to use Convolutional Neural Networks ({CNN}s) to regress a cell spatial density across the image. This is applicable to situations where traditional single-cell segmentation based methods do not work well due to cell clumping or overlap.},
	language = {en},
	author = {Xie, Weidi and Noble, J Alison and Zisserman, Andrew},
	pages = {8}
}

@article{dubost_3d_2018,
	title = {3D {Regression} {Neural} {Network} for the {Quantification} of {Enlarged} {Perivascular} {Spaces} in {Brain} {MRI}},
	url = {http://arxiv.org/abs/1802.05914},
	abstract = {Enlarged perivascular spaces (EPVS) in the brain are an emerging imaging marker for cerebral small vessel disease, and have been shown to be related to increased risk of various neurological diseases, including stroke and dementia. Automatic quantification of EPVS would greatly help to advance research into its etiology and its potential as a risk indicator of disease. We propose a convolutional network regression method to quantify the extent of EPVS in the basal ganglia from 3D brain MRI. We first segment the basal ganglia and subsequently apply a 3D convolutional regression network designed for small object detection within this region of interest. The network takes an image as input, and outputs a quantification score of EPVS. The network has significantly more convolution operations than pooling ones and no final activation, allowing it to span the space of real numbers. We validated our approach using a dataset of 2000 brain MRI scans scored visually. Experiments with varying sizes of training and test sets showed that a good performance can be achieved with a training set of only 200 scans. With a training set of 1000 scans, the intraclass correlation coefficient (ICC) between our scoring method and the expert's visual score was 0.74. Our method outperforms by a large margin - more than 0.10 - four more conventional automated approaches based on intensities, scale-invariant feature transform, and random forest. We show that the network learns the structures of interest and investigate the influence of hyper-parameters on the performance. We also evaluate the reproducibility of our network using a set of 60 subjects scanned twice (scan-rescan reproducibility). On this set our network achieves an ICC of 0.93, while the intrarater agreement reaches 0.80. Furthermore, the automatic EPVS scoring correlates similarly to age as visual scoring.},
	urldate = {2019-12-10},
	journal = {arXiv:1802.05914 [cs]},
	author = {Dubost, Florian and Adams, Hieab and Bortsova, Gerda and Ikram, M. Arfan and Niessen, Wiro and Vernooij, Meike and de Bruijne, Marleen},
	month = oct,
	year = {2018},
	note = {arXiv: 1802.05914},
	keywords = {Computer Science - Computer Vision and Pattern Recognition}
}

@article{kim_mumford-shah_2019,
	title = {Mumford-{Shah} {Loss} {Functional} for {Image} {Segmentation} with {Deep} {Learning}},
	url = {http://arxiv.org/abs/1904.02872},
	abstract = {Recent state-of-the-art image segmentation algorithms are mostly based on deep neural networks, thanks to their high performance and fast computation time. However, these methods are usually trained in a supervised manner, which requires large number of high quality ground-truth segmentation masks. On the other hand, classical image segmentation approaches such as level-set methods are formulated in a self-supervised manner by minimizing energy functions such as Mumford-Shah functional, so they are still useful to help generation of segmentation masks without labels. Unfortunately, these algorithms are usually computationally expensive and often have limitation in semantic segmentation. In this paper, we propose a novel loss function based on Mumford-Shah functional that can be used in deep-learning based image segmentation without or with small labeled data. This loss function is based on the observation that the softmax layer of deep neural networks has striking similarity to the characteristic function in the Mumford-Shah functional. We show that the new loss function enables semi-supervised and unsupervised segmentation. In addition, our loss function can be also used as a regularized function to enhance supervised semantic segmentation algorithms. Experimental results on multiple datasets demonstrate the effectiveness of the proposed method.},
	urldate = {2019-12-10},
	journal = {arXiv:1904.02872 [cs, stat]},
	author = {Kim, Boah and Ye, Jong Chul},
	month = sep,
	year = {2019},
	note = {arXiv: 1904.02872},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning}
}

@inproceedings{bertels_optimizing_2019,
	address = {Cham},
	series = {Lecture {Notes} in {Computer} {Science}},
	title = {Optimizing the {Dice} {Score} and {Jaccard} {Index} for {Medical} {Image} {Segmentation}: {Theory} and {Practice}},
	isbn = {978-3-030-32245-8},
	shorttitle = {Optimizing the {Dice} {Score} and {Jaccard} {Index} for {Medical} {Image} {Segmentation}},
	doi = {10.1007/978-3-030-32245-8_11},
	abstract = {The Dice score and Jaccard index are commonly used metrics for the evaluation of segmentation tasks in medical imaging. Convolutional neural networks trained for image segmentation tasks are usually optimized for (weighted) cross-entropy. This introduces an adverse discrepancy between the learning optimization objective (the loss) and the end target metric. Recent works in computer vision have proposed soft surrogates to alleviate this discrepancy and directly optimize the desired metric, either through relaxations (soft-Dice, soft-Jaccard) or submodular optimization (Lovász-softmax). The aim of this study is two-fold. First, we investigate the theoretical differences in a risk minimization framework and question the existence of a weighted cross-entropy loss with weights theoretically optimized to surrogate Dice or Jaccard. Second, we empirically investigate the behavior of the aforementioned loss functions w.r.t. evaluation with Dice score and Jaccard index on five medical segmentation tasks. Through the application of relative approximation bounds, we show that all surrogates are equivalent up to a multiplicative factor, and that no optimal weighting of cross-entropy exists to approximate Dice or Jaccard measures. We validate these findings empirically and show that, while it is important to opt for one of the target metric surrogates rather than a cross-entropy-based loss, the choice of the surrogate does not make a statistical difference on a wide range of medical segmentation tasks.},
	language = {en},
	booktitle = {Medical {Image} {Computing} and {Computer} {Assisted} {Intervention} – {MICCAI} 2019},
	publisher = {Springer International Publishing},
	author = {Bertels, Jeroen and Eelbode, Tom and Berman, Maxim and Vandermeulen, Dirk and Maes, Frederik and Bisschops, Raf and Blaschko, Matthew B.},
	editor = {Shen, Dinggang and Liu, Tianming and Peters, Terry M. and Staib, Lawrence H. and Essert, Caroline and Zhou, Sean and Yap, Pew-Thian and Khan, Ali},
	year = {2019},
	keywords = {Cross-entropy, Dice, Jaccard, Risk minimization},
	pages = {92--100}
}
% url = {http://papers.nips.cc/paper/8169-an-intriguing-failing-of-convolutional-neural-networks-and-the-coordconv-solution.pdf}
@incollection{liu_intriguing_2018,
title = {An intriguing failing of convolutional neural networks and the CoordConv solution},
author = {Liu, Rosanne and Lehman, Joel and Molino, Piero and Petroski Such, Felipe and Frank, Eric and Sergeev, Alex and Yosinski, Jason},
booktitle = {Neural Information Processing Systems},
pages = {9605--9616},
year = {2018},

}
@article{mortazi_weakly_2019,
	title = {Weakly {Supervised} {Segmentation} by {A} {Deep} {Geodesic} {Prior}},
	url = {http://arxiv.org/abs/1908.06498},
	abstract = {The performance of the state-of-the-art image segmentation methods heavily relies on the high-quality annotations, which are not easily affordable, particularly for medical data. To alleviate this limitation, in this study, we propose a weakly supervised image segmentation method based on a deep geodesic prior. We hypothesize that integration of this prior information can reduce the adverse effects of weak labels in segmentation accuracy. Our proposed algorithm is based on a prior information, extracted from an auto-encoder, trained to map objects geodesic maps to their corresponding binary maps. The obtained information is then used as an extra term in the loss function of the segmentor. In order to show efficacy of the proposed strategy, we have experimented segmentation of cardiac substructures with clean and two levels of noisy labels (L1, L2). Our experiments showed that the proposed algorithm boosted the performance of baseline deep learning-based segmentation for both clean and noisy labels by 4.4\%, 4.6\%(L1), and 6.3\%(L2) in dice score, respectively. We also showed that the proposed method was more robust in the presence of high-level noise due to the existence of shape priors.},
	urldate = {2019-12-03},
	journal = {arXiv:1908.06498 [cs, stat]},
	author = {Mortazi, Aliasghar and Khosravan, Naji and Torigian, Drew A. and Kurugol, Sila and Bagci, Ulas},
	month = aug,
	year = {2019},
	note = {arXiv: 1908.06498},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning}
}
% url = {http://arxiv.org/abs/1812.07032},


@article{lee_tetris:_2019,
	title = {{TeTrIS}: {Template} {Transformer} {Networks} for {Image} {Segmentation} {With} {Shape} {Priors}},
	volume = {38},
	issn = {1558-254X},
	shorttitle = {{TeTrIS}},
	doi = {10.1109/TMI.2019.2905990},
	abstract = {In this paper, we introduce and compare different approaches for incorporating shape prior information into neural network-based image segmentation. Specifically, we introduce the concept of template transformer networks, where a shape template is deformed to match the underlying structure of interest through an end-to-end trained spatial transformer network. This has the advantage of explicitly enforcing shape priors, and this is free of discretization artifacts by providing a soft partial volume segmentation. We also introduce a simple yet effective way of incorporating priors in the state-of-the-art pixel-wise binary classification methods such as fully convolutional networks and U-net. Here, the template shape is given as an additional input channel, incorporating this information significantly reduces false positives. We report results on synthetic data and sub-voxel segmentation of coronary lumen structures in cardiac computed tomography showing the benefit of incorporating priors in neural network-based image segmentation.},
	number = {11},
	journal = {IEEE Transactions on Medical Imaging},
	author = {Lee, Matthew Chung Hai and Petersen, Kersten and Pawlowski, Nick and Glocker, Ben and Schaap, Michiel},
	month = nov,
	year = {2019},
	keywords = {Deformable models, Image segmentation, Neural networks, Shape, Strain, Task analysis, Training, computerised tomography, end-to-end trained spatial transformer network, fully convolutional networks, image classification, image registration, image segmentation, learning (artificial intelligence), medical image processing, neural nets, neural network-based image segmentation, neural networks, shape prior information, shape priors, shape template, soft partial volume segmentation, state-of-the-art pixel-wise binary classification methods, sub-voxel segmentation, template deformation, template shape, template transformer networks},
	pages = {2596--2606}
}

@article{hoyer_grid_2019,
	title = {Grid {Saliency} for {Context} {Explanations} of {Semantic} {Segmentation}},
	url = {http://arxiv.org/abs/1907.13054},
	abstract = {Recently, there has been a growing interest in developing saliency methods that provide visual explanations of network predictions. Still, the usability of existing methods is limited to image classification models. To overcome this limitation, we extend the existing approaches to generate grid saliencies, which provide spatially coherent visual explanations for (pixel-level) dense prediction networks. As the proposed grid saliency allows to spatially disentangle the object and its context, we specifically explore its potential to produce context explanations for semantic segmentation networks, discovering which context most influences the class predictions inside a target object area. We investigate the effectiveness of grid saliency on a synthetic dataset with an artificially induced bias between objects and their context as well as on the real-world Cityscapes dataset using state-of-the-art segmentation networks. Our results show that grid saliency can be successfully used to provide easily interpretable context explanations and, moreover, can be employed for detecting and localizing contextual biases present in the data.},
	urldate = {2019-11-24},
	journal = {arXiv:1907.13054 [cs]},
	author = {Hoyer, Lukas and Munoz, Mauricio and Katiyar, Prateek and Khoreva, Anna and Fischer, Volker},
	month = nov,
	year = {2019},
	note = {arXiv: 1907.13054},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning}
}

@article{patra_saliency_nodate,
	title = {Saliency aware continual learning in medical image analysis},
	abstract = {While medical image analysis has seen extensive use of deep neural networks, learning over multiple tasks is a challenge for connectionist networks due to tendencies of degradation in performance over old tasks while adapting to novel tasks. It is pertinent that adaptations to new data distributions over time are tractable with automated analysis methods as medical imaging data acquisition is typically not a static problem. So, one needs to ensure that a continual learning paradigm be ensured in machine learning methods deployed for medical imaging. To explore interpretable lifelong learning for deep neural networks in medical imaging, we introduce a perspective of understanding forgetting in neural networks through the notions of attention and saliency. Concretely, we propose quantiﬁcation of forgetting as a decline in the quality of class speciﬁc saliency maps after each subsequent task schedule. We also introduce a knowledge transfer from past tasks to present by a saliency guided retention of past exemplars which help augment the ability to retain past knowledge while optimising parameters for current tasks. Experiments on a clinical fetal echocardiography dataset demonstrate a state-of-the-art performance for our protocols.},
	language = {en},
	author = {Patra, Arijit and Noble, J Alison},
	pages = {13}
}

@misc{noauthor_[1907.13054]_nodate,
	title = {[1907.13054] {Grid} {Saliency} for {Context} {Explanations} of {Semantic} {Segmentation}},
	url = {https://arxiv.org/abs/1907.13054},
	urldate = {2019-11-22}
}


@article{bach_pixel-wise_2015,
	title = {On {Pixel}-{Wise} {Explanations} for {Non}-{Linear} {Classifier} {Decisions} by {Layer}-{Wise} {Relevance} {Propagation}},
	volume = {10},
	issn = {1932-6203},
	url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0130140},
	doi = {10.1371/journal.pone.0130140},
	abstract = {Understanding and interpreting classification decisions of automated image classification systems is of high value in many applications, as it allows to verify the reasoning of the system and provides additional information to the human expert. Although machine learning methods are solving very successfully a plethora of tasks, they have in most cases the disadvantage of acting as a black box, not providing any information about what made them arrive at a particular decision. This work proposes a general solution to the problem of understanding classification decisions by pixel-wise decomposition of nonlinear classifiers. We introduce a methodology that allows to visualize the contributions of single pixels to predictions for kernel-based classifiers over Bag of Words features and for multilayered neural networks. These pixel contributions can be visualized as heatmaps and are provided to a human expert who can intuitively not only verify the validity of the classification decision, but also focus further analysis on regions of potential interest. We evaluate our method for classifiers trained on PASCAL VOC 2009 images, synthetic image data containing geometric shapes, the MNIST handwritten digits data set and for the pre-trained ImageNet model available as part of the Caffe open source package.},
	language = {en},
	number = {7},
	urldate = {2019-11-19},
	journal = {PLOS ONE},
	author = {Bach, Sebastian and Binder, Alexander and Montavon, Grégoire and Klauschen, Frederick and Müller, Klaus-Robert and Samek, Wojciech},
	year = {2015},
	keywords = {Algorithms, Coding mechanisms, Imaging techniques, Kernel functions, Kernel methods, Neural networks, Neurons, Vision},
	pages = {e0130140}
}

@inproceedings{yuille_prior-aware_2019,
	title = {Prior-aware {Neural} {Network} for {Partially}-{Supervised} {Multi}-{Organ} {Segmentation}},
	url = {https://arxiv.org/abs/1904.06346},
	urldate = {2019-11-19},
	booktitle = {International {Conference} on {Computer} {Vision}},
	author = {Yuille, Alan and Wang, Chong and Fishman, Elliot and Han, Mei and Bai, Song and Chen, Xinlei and Zhou, Yuyin and Li, Zhe},
	year = {2019}
}

@inproceedings{rezatofighi_generalized_2019,
	title = {Generalized {Intersection} {Over} {Union}: {A} {Metric} and a {Loss} for {Bounding} {Box} {Regression}},
	shorttitle = {Generalized {Intersection} {Over} {Union}},
	url = {http://openaccess.thecvf.com/content_CVPR_2019/html/Rezatofighi_Generalized_Intersection_Over_Union_A_Metric_and_a_Loss_for_CVPR_2019_paper.html},
	urldate = {2019-11-13},
	author = {Rezatofighi, Hamid and Tsoi, Nathan and Gwak, JunYoung and Sadeghian, Amir and Reid, Ian and Savarese, Silvio},
	year = {2019},
	pages = {658--666}
}

@misc{noauthor_[1908.01373]_nodate,
	title = {[1908.01373] {Unsupervised} {Microvascular} {Image} {Segmentation} {Using} an {Active} {Contours} {Mimicking} {Neural} {Network}},
	url = {https://arxiv.org/abs/1908.01373},
	urldate = {2019-11-07}
}

@misc{noauthor_disentangling_nodate,
	title = {Disentangling {Factors} of {Variation} {Using} {Few} {Labels}},
	url = {https://arxiv.org/abs/1905.01258},
	urldate = {2019-10-28}
}
@article{ke_multi-task_2019,
	title = {A multi-task {U}-net for segmentation with lazy labels},
	url = {http://arxiv.org/abs/1906.12177},
	abstract = {The need for labour intensive pixel-wise annotation is a major limitation of many fully supervised learning methods for image segmentation. In this paper, we propose a deep convolutional neural network for multi-class segmentation that circumvents this problem by being trainable on coarse data labels combined with only a very small number of images with pixel-wise annotations. We call this new labelling strategy 'lazy' labels. Image segmentation is then stratified into three connected tasks: rough detection of class instances, separation of wrongly connected objects without a clear boundary, and pixel-wise segmentation to find the accurate boundaries of each object. These problems are integrated into a multitask learning framework and the model is trained end-to-end in a semi-supervised fashion. The method is applied on a dataset of food microscopy images. We show that the model gives accurate segmentation results even if exact boundary labels are missing for a majority of the annotated data. This allows more flexibility and efficiency for training deep neural networks that are data hungry in a practical setting where manual annotation is expensive, by collecting more lazy (rough) annotations than precisely segmented images.},
	urldate = {2019-11-07},
	journal = {arXiv:1906.12177 [cs, stat]},
	author = {Ke, Rihuan and Bugeau, Aurélie and Papadakis, Nicolas and Schuetz, Peter and Schönlieb, Carola-Bibiane},
	month = jun,
	year = {2019},
	note = {arXiv: 1906.12177},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning}
}

@inproceedings{liu_ganredl:_2019,
	address = {Cham},
	series = {Lecture {Notes} in {Computer} {Science}},
	title = {{GANReDL}: {Medical} {Image} {Enhancement} {Using} a {Generative} {Adversarial} {Network} with {Real}-{Order} {Derivative} {Induced} {Loss} {Functions}},
	isbn = {978-3-030-32248-9},
	shorttitle = {{GANReDL}},
	doi = {10.1007/978-3-030-32248-9_13},
	abstract = {Deep (convolutional) neural networks (D{CNN}) have recently gained popularity, and shown improved performance in the field of image enhancement (de-noising and super-resolution, for instance). However, the central issue of recovering finer texture details in images still remains unsolved. State-of-the-art objective functions used in D{CNN} mostly focus on minimizing the mean squared reconstruction error. The resulting image estimates have high peak signal-to-noise ratios, but they are often lacking high-frequency details, and are therefore error-prone with respect to fine-scale, possibly clinically relevant details.In this article, we present GANReDL, a generative adversarial network (GAN) for image enhancement equipped with a real-order derivative induced loss functions (ReDL) which we will show gives improved images, in particular wrt to the reconstruction of fine-scale details. To the best of our knowledge, this is the first framework that incorporates non-integer order derivatives in loss functions. To this aim, we propose a discriminator network that is trained to differentiate between the enhanced images and ground-truth images, and propose a new loss function motivated by real-order derivatives which is capable of also capturing global image features rather than pixel-wise features only. We show, with several numerical experiments, that GANReDL is better in reconstructing the high-frequency image details, and therefore show improved performance for image enhancement over other state-of-the-art methods.},
	language = {en},
	booktitle = {Medical {Image} {Computing} and {Computer} {Assisted} {Intervention} – {MICCAI} 2019},
	publisher = {Springer International Publishing},
	author = {Liu, Pan and Li, Chao and Schönlieb, Carola-Bibiane},
	editor = {Shen, Dinggang and Liu, Tianming and Peters, Terry M. and Staib, Lawrence H. and Essert, Caroline and Zhou, Sean and Yap, Pew-Thian and Khan, Ali},
	year = {2019},
	keywords = {Generative adversarial networks, Medical image super-resolution, Real order derivative operators},
	pages = {110--117}
}

@article{chaitanya_semi-supervised_2019,
	title = {Semi-{Supervised} and {Task}-{Driven} {Data} {Augmentation}},
	url = {http://arxiv.org/abs/1902.05396},
	abstract = {Supervised deep learning methods for segmentation require large amounts of labelled training data, without which they are prone to overﬁtting, not generalizing well to unseen images. In practice, obtaining a large number of annotations from clinical experts is expensive and time-consuming. One way to address scarcity of annotated examples is data augmentation using random spatial and intensity transformations. Recently, it has been proposed to use generative models to synthesize realistic training examples, complementing the random augmentation. So far, these methods have yielded limited gains over the random augmentation. However, there is potential to improve the approach by (i) explicitly modeling deformation ﬁelds (non-aﬃne spatial transformation) and intensity transformations and (ii) leveraging unlabelled data during the generative process. With this motivation, we propose a novel task-driven data augmentation method where to synthesize new training examples, a generative network explicitly models and applies deformation ﬁelds and additive intensity masks on existing labelled data, modeling shape and intensity variations, respectively. Crucially, the generative model is optimized to be conducive to the task, in this case segmentation, and constrained to match the distribution of images observed from labelled and unlabelled samples. Furthermore, explicit modeling of deformation ﬁelds allow synthesizing segmentation masks and images in exact correspondence by simply applying the generated transformation to an input image and the corresponding annotation. Our experiments on cardiac magnetic resonance images (MRI) showed that, for the task of segmentation in small training data scenarios, the proposed method substantially outperforms conventional augmentation techniques.},
	language = {en},
	urldate = {2019-11-04},
	journal = {arXiv:1902.05396 [cs, stat]},
	author = {Chaitanya, Krishna and Karani, Neerav and Baumgartner, Christian and Donati, Olivio and Becker, Anton and Konukoglu, Ender},
	month = feb,
	year = {2019},
	note = {arXiv: 1902.05396},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning, veronika}
}

@article{taha_metrics_2015,
	title = {Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool},
	volume = {15},
	issn = {1471-2342},
	shorttitle = {Metrics for evaluating 3D medical image segmentation},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4533825/},
	doi = {10.1186/s12880-015-0068-x},
	abstract = {Background
Medical Image segmentation is an important image processing step. Comparing images to evaluate the quality of segmentation is an essential part of measuring progress in this research area. Some of the challenges in evaluating medical segmentation are: metric selection, the use in the literature of multiple definitions for certain metrics, inefficiency of the metric calculation implementations leading to difficulties with large volumes, and lack of support for fuzzy segmentation by existing metrics.

Result
First we present an overview of 20 evaluation metrics selected based on a comprehensive literature review. For fuzzy segmentation, which shows the level of membership of each voxel to multiple classes, fuzzy definitions of all metrics are provided. We present a discussion about metric properties to provide a guide for selecting evaluation metrics. Finally, we propose an efficient evaluation tool implementing the 20 selected metrics. The tool is optimized to perform efficiently in terms of speed and required memory, also if the image size is extremely large as in the case of whole body MRI or CT volume segmentation. An implementation of this tool is available as an open source project.

Conclusion
We propose an efficient evaluation tool for 3D medical image segmentation using 20 evaluation metrics and provide guidelines for selecting a subset of these metrics that is suitable for the data and the segmentation task.},
	urldate = {2019-11-04},
	journal = {BMC Medical Imaging},
	author = {Taha, Abdel Aziz and Hanbury, Allan},
	month = aug,
	year = {2015},
	pmid = {26263899},
	pmcid = {PMC4533825}
}

@article{tajbakhsh_embracing_2019,
	title = {Embracing {Imperfect} {Datasets}: {A} {Review} of {Deep} {Learning} {Solutions} for {Medical} {Image} {Segmentation}},
	shorttitle = {Embracing {Imperfect} {Datasets}},
	url = {http://arxiv.org/abs/1908.10454},
	abstract = {The medical imaging literature has witnessed remarkable progress in high-performing segmentation models based on convolutional neural networks. Despite the new performance highs, the recent advanced segmentation models still require large, representative, and high quality annotated datasets. However, rarely do we have a perfect training dataset, particularly in the field of medical imaging, where data and annotations are both expensive to acquire. Recently, a large body of research has studied the problem of medical image segmentation with imperfect datasets, tackling two major dataset limitations: scarce annotations where only limited annotated data is available for training, and weak annotations where the training data has only sparse annotations, noisy annotations, or image-level annotations. In this article, we provide a detailed review of the solutions above, summarizing both the technical novelties and empirical results. We further compare the benefits and requirements of the surveyed methodologies and provide our recommended solutions to the problems of scarce and weak annotations. We hope this review increases the community awareness of the techniques to handle imperfect datasets.},
	urldate = {2019-11-04},
	journal = {arXiv:1908.10454 [cs, eess]},
	author = {Tajbakhsh, Nima and Jeyaseelan, Laura and Li, Qian and Chiang, Jeffrey and Wu, Zhihao and Ding, Xiaowei},
	month = aug,
	year = {2019},
	note = {arXiv: 1908.10454
version: 1},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing}
}

@article{karimi_reducing_2019,
	title = {Reducing the {Hausdorff} {Distance} in {Medical} {Image} {Segmentation} with {Convolutional} {Neural} {Networks}},
	url = {http://arxiv.org/abs/1904.10030},
	abstract = {The Hausdorff Distance (HD) is widely used in evaluating medical image segmentation methods. However, existing segmentation methods do not attempt to reduce HD directly. In this paper, we present novel loss functions for training convolutional neural network ({CNN})-based segmentation methods with the goal of reducing HD directly. We propose three methods to estimate HD from the segmentation probability map produced by a {CNN}. One method makes use of the distance transform of the segmentation boundary. Another method is based on applying morphological erosion on the difference between the true and estimated segmentation maps. The third method works by applying circular/spherical convolution kernels of different radii on the segmentation probability maps. Based on these three methods for estimating HD, we suggest three loss functions that can be used for training to reduce HD. We use these loss functions to train {CNN}s for segmentation of the prostate, liver, and pancreas in ultrasound, magnetic resonance, and computed tomography images and compare the results with commonly-used loss functions. Our results show that the proposed loss functions can lead to approximately 18-45 \% reduction in HD without degrading other segmentation performance criteria such as the Dice similarity coefficient. The proposed loss functions can be used for training medical image segmentation methods in order to reduce the large segmentation errors.},
	urldate = {2019-11-04},
	journal = {arXiv:1904.10030 [cs, eess, stat]},
	author = {Karimi, Davood and Salcudean, Septimiu E.},
	month = apr,
	year = {2019},
	note = {arXiv: 1904.10030},
	keywords = {Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing, Statistics - Machine Learning}
}


@article{bateson_constrained_2019,
	title = {Constrained domain adaptation for segmentation},

	abstract = {We propose to adapt segmentation networks with a constrained formulation, which embeds domain-invariant prior knowledge about the segmentation regions. Such knowledge may take the form of simple anatomical information, e.g., structure size or shape, estimated from source samples or known a priori. Our method imposes domain-invariant inequality constraints on the network outputs of unlabeled target samples. It implicitly matches prediction statistics between target and source domains with permitted uncertainty of prior knowledge. We address our constrained problem with a differentiable penalty, fully suited for standard stochastic gradient descent approaches, removing the need for computationally expensive Lagrangian optimization with dual projections. Unlike current two-step adversarial training, our formulation is based on a single loss in a single network, which simplifies adaptation by avoiding extra adversarial steps, while improving convergence and quality of training. The comparison of our approach with state-of-the-art adversarial methods reveals substantially better performance on the challenging task of adapting spine segmentation across different MRI modalities. Our results also show a robustness to imprecision of size priors, approaching the accuracy of a fully supervised model trained directly in a target domain.Our method can be readily used for various constraints and segmentation problems.},
	urldate = {2019-10-27},
	journal = {arXiv:1908.02996 [cs]},
	author = {Bateson, Mathilde and Dolz, Jose and Kervadec, Hoel and Lombaert, Hervé and Ayed, Ismail Ben},
	month = aug,
	year = {2019},
	
	keywords = {Computer Science - Computer Vision and Pattern Recognition}
}

@article{Taghanaki2019,
  title={Deep Semantic Segmentation of Natural and Medical Images: A Review},
  author={Saeid Asgari Taghanaki and Kumar Abhishek and Joseph Paul Cohen and Julien Cohen-Adad and Ghassan Hamarneh},
  journal={ArXiv},
  year={2019},
  volume={abs/1910.07655}
}

@article{Haq2020,
title = "Deep learning approaches to biomedical image segmentation",
journal = "Informatics in Medicine Unlocked",
volume = "18",
pages = "100297",
year = "2020",
issn = "2352-9148",
doi = "https://doi.org/10.1016/j.imu.2020.100297",
url = "http://www.sciencedirect.com/science/article/pii/S235291481930214X",
author = "Intisar Haque and Jeremiah Neubert",
keywords = "Image segmentation, Deep learning, Machine learning, Biomedical images",
abstract = "The review covers automatic segmentation of images by means of deep learning approaches in the area of medical imaging. Current developments in machine learning, particularly related to deep learning, are proving instrumental in identification, and quantification of patterns in the medical images. The pivotal point of these advancements is the essential capability of the deep learning approaches to obtain hierarchical feature representations directly from the images, which in turn is eliminating the need for handcrafted features. Deep learning is expeditiously turning into the state-of-the-art for medical image processing and has resulted in performance improvements in diverse clinical applications. In this review, the basics of deep learning methods are discussed along with an overview of successful implementations involving image segmentation for different medical applications. Finally, some research issues are highlighted and the future need for further improvements is pointed out."
}



@article{LITJENS2017,
title = "A survey on deep learning in medical image analysis",
journal = "Medical Image Analysis",
volume = "42",
pages = "60 - 88",
year = "2017",
issn = "1361-8415",
doi = "https://doi.org/10.1016/j.media.2017.07.005",
url = "http://www.sciencedirect.com/science/article/pii/S1361841517301135",
author = "Geert Litjens and Thijs Kooi and Babak Ehteshami Bejnordi and Arnaud Arindra Adiyoso Setio and Francesco Ciompi and Mohsen Ghafoorian and Jeroen A.W.M. [van der Laak] and Bram [van Ginneken] and Clara I. Sánchez",
keywords = "Deep learning, Convolutional neural networks, Medical imaging, Survey",
abstract = "Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks. Concise overviews are provided of studies per application area: neuro, retinal, pulmonary, digital pathology, breast, cardiac, abdominal, musculoskeletal. We end with a summary of the current state-of-the-art, a critical discussion of open challenges and directions for future research."
}

@article{Hesamian2019,
author = {Hesamian, Mohammad Hesam and Jia, Wenjing and He, Xiangjian and Kennedy, Paul},
year = {2019},
month = {05},
pages = {},
title = {Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges},
volume = {32},
journal = {Journal of Digital Imaging},
doi = {10.1007/s10278-019-00227-x}
}


@article{Pouyanfar2018,
author = {Pouyanfar, Samira and Sadiq, Saad and Yan, Yilin and Tian, Haiman and Tao, Yudong and Reyes, Maria Presa and Shyu, Mei-Ling and Chen, Shu-Ching and Iyengar, S. S.},
title = {A Survey on Deep Learning: Algorithms, Techniques, and Applications},
year = {2018},
issue_date = {January 2019},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {51},
number = {5},
issn = {0360-0300},
url = {https://doi.org/10.1145/3234150},
doi = {10.1145/3234150},
journal = {ACM Comput. Surv.},
month = sep,
articleno = {Article 92},
numpages = {36},
keywords = {machine learning, Deep learning, neural networks, distributed processing, big data, survey}
}

@article{Jiang2017,
  title={Medical image semantic segmentation based on deep learning},
  author={Feng Jiang and Aleksei Grigorev and Seungmin Rho and Zhihong Tian and Yunsheng Fu and Worku Jifara Sori and Adil Khan and Shaohui Liu},
  journal={Neural Comput Appl},
  year={2017},
  volume={29},
  pages={1257-1265}
}
@Inbook{Havaei2016,
author="Havaei, Mohammad
and Guizard, Nicolas
and Larochelle, Hugo
and Jodoin, Pierre-Marc",
editor="Holzinger, Andreas",
title="Deep Learning Trends for Focal Brain Pathology Segmentation in MRI",
booktitle="Machine Learning for Health Informatics: State-of-the-Art and Future Challenges",
year="2016",
pages="125--148",
abstract="Segmentation of focal (localized) brain pathologies such as brain tumors and brain lesions caused by multiple sclerosis and ischemic strokes are necessary for medical diagnosis, surgical planning and disease development as well as other applications such as tractography. Over the years, attempts have been made to automate this process for both clinical and research reasons. In this regard, machine learning methods have long been a focus of attention. Over the past two years, the medical imaging field has seen a rise in the use of a particular branch of machine learning commonly known as deep learning. In the non-medical computer vision world, deep learning based methods have obtained state-of-the-art results on many datasets. Recent studies in computer aided diagnostics have shown deep learning methods (and especially convolutional neural networks - CNN) to yield promising results. In this chapter, we provide a survey of CNN methods applied to medical imaging with a focus on brain pathology segmentation. In particular, we discuss their characteristic peculiarities and their specific configuration and adjustments that are best suited to segment medical images. We also underline the intrinsic differences deep learning methods have with other machine learning methods.",
isbn="978-3-319-50478-0",
doi="10.1007/978-3-319-50478-0_6",
url="https://doi.org/10.1007/978-3-319-50478-0_6"
}

@article{Huang2016,
  title={Breast ultrasound image segmentation: a survey},
  author={Qinghua Huang and Yaozhong Luo and Qiangzhi Zhang},
  journal={International Journal of Computer Assisted Radiology and Surgery},
  year={2016},
  volume={12},
  pages={493-507}
}

@article{Nosrati2016,
  author    = {Masoud S. Nosrati and
               Ghassan Hamarneh},
  title     = {Incorporating prior knowledge in medical image segmentation: a survey},
  journal   = {CoRR},
  volume    = {abs/1607.01092},
  year      = {2016},
  url       = {http://arxiv.org/abs/1607.01092},
  archivePrefix = {arXiv},
  eprint    = {1607.01092},
  timestamp = {Mon, 13 Aug 2018 16:48:36 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/NosratiH16.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}





% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%losses%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

@article{clDice2020,
  title={clDice - a Topology-Preserving Loss Function for Tubular Structure Segmentation},
  author={Suprosanna Shit and Johannes Christian Paetzold and Anjany Sekuboyina and Andrey Zhylka and Ivan Ezhov and Alexander Unger and Josien P. W. Pluim and Giles Tetteh and Bjoern H. Menze},
  journal={ArXiv},
  year={2020},
  volume={abs/2003.07311}
}

@article{Xue2019,
  title={Shape-Aware Organ Segmentation by Predicting Signed Distance Maps},
  author={Yuan Xue and Hui Tang and Zhi Qiao and Guanzhong Gong and Yong Yin and Zhenyu Qian and Chao Huang and Wei Fan and Xiaolei Huang},
  journal={ArXiv},
  year={2019},
  volume={abs/1912.03849}
}


@article{hu_NIPS2019,
	title = {Topology-{Preserving} {Deep} {Image} {Segmentation}},
	url = {http://arxiv.org/abs/1906.05404},
	abstract = {Segmentation algorithms are prone to make topological errors on fine-scale structures, e.g., broken connections. We propose a novel method that learns to segment with correct topology. In particular, we design a continuous-valued loss function that enforces a segmentation to have the same topology as the ground truth, i.e., having the same Betti number. The proposed topology-preserving loss function is differentiable and we incorporate it into end-to-end training of a deep neural network. Our method achieves much better performance on the Betti number error, which directly accounts for the topological correctness. It also performs superiorly on other topology-relevant metrics, e.g., the Adjusted Rand Index and the Variation of Information. We illustrate the effectiveness of the proposed method on a broad spectrum of natural and biomedical datasets.},
	urldate = {2019-12-12},
	journal = {arXiv:1906.05404 [cs]},
	author = {Hu, Xiaoling and Fuxin, Li and Samaras, Dimitris and Chen, Chao},
	month = jun,
	year = {2019},
	note = {arXiv: 1906.05404},
	keywords = {Computer Science - Computational Geometry, Computer Science - Computer Vision and Pattern Recognition}
}

@ARTICLE{Cardiac2020,
  
AUTHOR={Chen, Chen and Qin, Chen and Qiu, Huaqi and Tarroni, Giacomo and Duan, Jinming and Bai, Wenjia and Rueckert, Daniel},   
	 
TITLE={Deep Learning for Cardiac Image Segmentation: A Review},      
	
JOURNAL={Frontiers in Cardiovascular Medicine},      
	
VOLUME={7},      

PAGES={25},     
	
YEAR={2020},      
	  
URL={https://www.frontiersin.org/article/10.3389/fcvm.2020.00025},       
	
DOI={10.3389/fcvm.2020.00025},      
	
ISSN={2297-055X},   
   
ABSTRACT={Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound and major anatomical structures of interest (ventricles, atria, and vessels). In addition, a summary of publicly available cardiac image datasets and code repositories are included to provide a base for encouraging reproducible research. Finally, we discuss the challenges and limitations with current deep learning-based approaches (scarcity of labels, model generalizability across different domains, interpretability) and suggest potential directions for future research.}
}
@inproceedings{Razzak2018, 
author = {Razzak, Muhammad and Naz, Saeeda and Zaib, Ahmad},
year = {2018},
month = {01},
pages = {323-350},
title = {Deep Learning for Medical Image Processing: Overview, Challenges and the Future},
isbn = {978-3-319-65980-0},
booktitle = {Lecture Notes in Computational Vision and Biomechanics},
doi = {10.1007/978-3-319-65981-7_12}
}


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%GITHUB %%%%%%%%%%%%%%%%%%%%%%%%%%%%%ÙÙÙ

@misc{Yu_Losses,
  author = {Zhen Yu},
  title = {Project Title},
  year = {2013},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/Zakiyi/Medical-Image-Segmentation-Loss}},
  commit = {}
}


@misc{SegLOss,
  author = {JunMa11},
  title = {},
  year = {},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/JunMa11/SegLoss}},
  commit = {}
}
@misc{SegLOss_2,
  author = {JunMa11},
  title = {},
  year = {},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/JunMa11/SOTA-MedSeg}},
  commit = {}
}


@misc{MedSeg,
  author = {Shaofeng Yuan},
  title = {},
  year = {},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/shawnyuen/DeepLearningInMedicalImagingAndMedicalImageAnalysis}},
  commit = {}
}


@misc{Micaii_openSOurce,
  author = {JunMa11},
  title = {},
  year = {},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/JunMa11/MICCAI-OpenSourcePapers}},
  commit = {}
}





%%%%%%%%%%%%%%%%%%%%%%%%%   Ftting loss variants %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
@inproceedings{yang2019_MIDL,
title={Major Vessel Segmentation on X-ray Coronary Angiography using Deep Networks with a Novel Penalty Loss Function},
author={Su Yang and Jihoon Kweon and Young-Hak Kim},
booktitle={International Conference on Medical Imaging with Deep Learning -- Extended Abstract Track},
address={London, UK},
year={2019},
month={08--10 Jul},
url={https://openreview.net/forum?id=H1lTh8unKN},
abstract={In  this  study,  we  proposed  a  segmentation  method  of  major  vessels  on  X-ray  coronary angiography using fully convolutional networks based on U-Net architecture.  A novel loss function pGD was  introduced  by  adding  a  term  for  penalizing  false  negative  and  false positive to generalized dice coefficient (GD).  DenseNet121 with pGD achieved the highest average DSC of 91.9{\ensuremath{\pm}}8.7{\%}, precision of 91.3{\ensuremath{\pm}}8.8{\%}, and recall of 92.6{\ensuremath{\pm}}9.6{\%}, respectively and showed improved segmentation performance compared to GD.},
}}

@inbook{Wang2019,  
title     = "Automatic Segmentation of Vestibular Schwannoma from T2-Weighted MRI by Deep Spatial Attention with Hardness-Weighted Loss", 
abstract  = "Automatic segmentation of vestibular schwannoma (VS) tu-mors from magnetic resonance imaging (MRI) would facilitate efficientand accurate volume measurement to guide patient management andimprove clinical workflow. The accuracy and robustness is challenged bylow contrast, small target region and low through-plane resolution. Weintroduce a 2.5D convolutional neural network (CNN) able to exploit thedifferent in-plane and through-plane resolutions encountered in standardof care imaging protocols. We propose an attention module with explicitsupervision on the attention maps to enable the CNN to focus on thesmall target for more accurate segmentation. We also propose a hardness-weighted Dice loss function that gives higher weights to harder voxels toboost the training of CNNs. Experiments with ablation studies on theVS tumor segmentation task show that: 1) our 2.5D CNN outperformsits 2D and 3D counterparts, 2) our supervised attention mechanism out-performs unsupervised attention, 3) the voxel-level hardness-weightedDice loss improves the segmentation accuracy. Our method achieved anaverage Dice score and ASSD of 0.87 and 0.43 mm respectively. This willfacilitate patient management decisions in clinical practice.",  author    = "Guotai Wang and Jonathan Shapey and Wenqi Li and Dorent, {Reuben Philippe Robert} and Alex Demitriadis and Sotirios Bisdas and Ian Paddick and Robert Bradford and Shaoting Zhang and Sebastien Ourselin and Tom Vercauteren",  year      = "2019",  month     = "6",  day       = "5",  language  = "English",  booktitle = "Medical Image Computing and Computer Assisted Intervention – MICCAI 2019", }

@inproceedings{Wong2018_DSW,
  title={3D Segmentation with Exponential Logarithmic Loss for Highly Unbalanced Object Sizes},
  author={Ken C. L. Wong and Mehdi Moradi and Hui Tang and Tanveer F. Syeda-Mahmood},
  booktitle={MICCAI},
  year={2018}
}



%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%losses + structures %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
@article{LI2016_BSUNET,  
title     = "Bottleneck feature supervised U-Net for pixel-wise liver and tumor segmentation", keywords  = "Bottleneck, CNN, Encoding, Liver tumor, Segmentation, U-Net",  
author    = "Song LI and TSO, {Geoffrey K.F.} and Kaijian HE",  
year      = "2019",  
month     = "12", 
day       = "11",  
doi       = "10.1016/j.eswa.2019.113131",  
language  = "English",  volume    = "145", 
journal   = "Expert Systems with Applications",  issn      = "0957-4174",  publisher = "PERGAMON-ELSEVIER SCIENCE LTD", }


@misc{Abraham_Khan_2018, 
title={A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation}, author={Abraham, Nabila and Khan, Naimul Mefraz}, year={2018}}





%%%%%%%%%%%%%%%%%%%%%%%%%%%ùundecided %%%%%%%%%%%%%%%%%
@InProceedings{Wang2018_thresholdloss,
author="Wang, Na
and Bian, Cheng
and Wang, Yi
and Xu, Min
and Qin, Chenchen
and Yang, Xin
and Wang, Tianfu
and Li, Anhua
and Shen, Dinggang
and Ni, Dong",
editor="Frangi, Alejandro F.
and Schnabel, Julia A.
and Davatzikos, Christos
and Alberola-L{\'o}pez, Carlos
and Fichtinger, Gabor",
title="Densely Deep Supervised Networks with Threshold Loss for Cancer Detection in Automated Breast Ultrasound",
booktitle="Medical Image Computing and Computer Assisted Intervention -- MICCAI 2018",
year="2018",
publisher="Springer International Publishing",
address="Cham",
pages="641--648",
abstract="Automated breast ultrasound (ABUS) is a new and promising tool for diagnosing breast cancer. However, reviewing ABUS images is extremely time-consuming and oversight errors could happen. We propose a novel 3D convolutional network for automatic cancer detection in ABUS. Our contribution is twofold. First, we propose a threshold loss function to provide voxel-level adaptive threshold for discriminating cancer and non-cancer, thus achieving high sensitivity with low FPs. Second, we propose a densely deep supervision (DDS) mechanism to improve the sensitivity significantly by utilizing multi-scale discriminative features of all layers. Both class-balanced cross entropy loss and overlap loss are employed to enhance DDS performance. The efficacy of the proposed network is validated on a dataset of 196 patients with 661 cancer regions. The 4-fold cross-validation experiments show our network obtains a sensitivity of 93{\%} with 2.2 FPs per ABUS volume. Our proposed novel network can provide an accurate and automatic cancer detection tool for breast cancer screening by maintaining high sensitivity with low FPs.",
isbn="978-3-030-00937-3"
}



@article{Murugesan2019Psi_Net,
  title={Psi-Net: Shape and boundary aware joint multi-task deep network for medical image segmentation},
  author={Balamurali Murugesan and Kaushik Sarveswaran and Sharath M. Shankaranarayana and Keerthi Ram and Mohanasankar Sivaprakasam},
  journal={2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)},
  year={2019},
  pages={7223-7226}
}


@InProceedings{Oda2018_BESNET,
author="Oda, Hirohisa
and Roth, Holger R.
and Chiba, Kosuke
and Sokoli{\'{c}}, Jure
and Kitasaka, Takayuki
and Oda, Masahiro
and Hinoki, Akinari
and Uchida, Hiroo
and Schnabel, Julia A.
and Mori, Kensaku",
title="BESNet: Boundary-Enhanced Segmentation of Cells in Histopathological Images",
booktitle="MICCAI",
year="2018",
pages="228--236",
abstract="We propose a novel deep learning method called Boundary-Enhanced Segmentation Network (BESNet) for the detection and semantic segmentation of cells on histopathological images. The semantic segmentation of small regions using fully convolutional networks typically suffers from inaccuracies around the boundaries of small structures, like cells, because the probabilities often become blurred. In this work, we propose a new network structure that encodes input images to feature maps similar to U-net but utilizes two decoding paths that restore the original image resolution. One decoding path enhances the boundaries of cells, which can be used to improve the quality of the entire cell segmentation achieved in the other decoding path. We explore two strategies for enhancing the boundaries of cells: (1) skip connections of feature maps, and (2) adaptive weighting of loss functions. In (1), the feature maps from the boundary decoding path are concatenated with the decoding path for entire cell segmentation. In (2), an adaptive weighting of the loss for entire cell segmentation is performed when boundaries are not enhanced strongly, because detecting such parts is difficult. The detection rate of ganglion cells was 80.0{\%} with 1.0 false positives per histopathology slice. The mean Dice index representing segmentation accuracy was 74.0{\%}. BESNet produced a similar detection performance and higher segmentation accuracy than comparable U-net architectures without our modifications.",
isbn="978-3-030-00934-2"
}







% %%%%%%%%%%%%%%%%Hand crafter segmentation methods%%%%%%%%%%%%%ùù
@article{Indra2016,
author = {Maitra, Dr. Indra},
year = {2016},
month = {06},
pages = {317-327},
title = {A Survey on Relevant Edge Detection Technique for Medical Image Processing},
volume = {3},
journal = {European Journal of Biomedical and Pharmaceutical sciences}
}

@article{Senthilkumaran2016,
author = {N, Senthilkumaran and S, Vaithegi},
year = {2016},
month = {02},
pages = {1-13},
title = {Image Segmentation By Using Thresholding Techniques For Medical Images},
volume = {6},
journal = {Computer Science & Engineering: An International Journal},
doi = {10.5121/cseij.2016.6101}
}


@article{Prasantha2010,
author = {H.S.Prasantha, and Dr.Shashidhara.H.L, and Balasubramanya Murthy, Kannamedi and Madhavi, Latha},
year = {2010},
month = {07},
pages = {},
title = {MEDICAL IMAGE SEGMENTATION},
volume = {2},
journal = {International Journal on Computer Science and Engineering}
}


@inproceedings{Zhao2010,
author = {Zhao, Wencang and Zhang, Junbo and Li, Peilong and Li, Yang},
title = {Study of Image Segmentation Algorithm Based on Textural Features and Neural Network},
year = {2010},
isbn = {9780769540146},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ICICCI.2010.53},
doi = {10.1109/ICICCI.2010.53},
booktitle = {Proceedings of the 2010 International Conference on Intelligent Computing and Cognitive Informatics},
pages = {300–303},
numpages = {4},
keywords = {CT image, image recognition, image segmentation, neural network, textural characteristic},
series = {ICICCI ’10}
}

@article{Yasmin2013,
author = {Yasmin, Mussarat and Sharif, Muhammad and Mohsin, Sajjad},
year = {2013},
month = {01},
pages = {},
title = {Neural Networks in Medical Imaging Applications: A Survey},
volume = {22},
journal = {World Applied Sciences Journal}
}



@article{Sharif2013,
author = {Sharif, Muhammad and Raza, Mudassar and Mohsin, Sajjad and Shah, Jamal},
year = {2013},
month = {03},
pages = {1700-1703},
title = {Microscopic Feature Extraction Method},
volume = {4},
journal = {Int. J. Advanced Networking and Applications}
}






%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%ùlosses%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%ù

@InProceedings{Kervadec2019_BoundaryLoss,
  title = 	 {Boundary loss for highly unbalanced segmentation},
  author = 	 {Kervadec, Hoel and Bouchtiba, Jihene and Desrosiers, Christian and Granger, Eric and Dolz, Jose and {Ben Ayed}, Ismail},
  booktitle = 	 {Medical Imaging with Deep Learning},
  pages = 	 {285--296},
  year = 	 {2019},
  volume = 	 {102},
  series = 	 {Proceedings of Machine Learning Research},
  address = 	 {London, UK},
  month = 	 {08--10 Jul},
  publisher = 	 {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v102/kervadec19a/kervadec19a.pdf},
  abstract = 	 {Widely used loss functions for convolutional neural network (CNN) segmentation, e.g., Dice or cross-entropy, are based on integrals (summations) over the segmentation regions. Unfortunately, for  highly unbalanced segmentations, such regional losses have values that differ considerably – typically of several orders of magnitude – across segmentation classes, which may affect training performance and stability. We propose a boundary loss, which takes the form of a distance metric on the space of contours (or shapes), not regions. This can mitigate the difficulties of regional losses in the context of highly unbalanced segmentation problems because it uses integrals over the boundary (interface) between regions instead of unbalanced integrals over regions. Furthermore, a boundary loss provides information that is complimentary to regional losses. Unfortunately, it is not straightforward to represent the boundary points corresponding to the regional softmax outputs of a CNN. Our boundary loss is inspired by discrete (graph-based) optimization techniques for computing gradient flows of curve evolution. Following an integral approach for computing boundary variations, we express a non-symmetric $L_2$ distance on the space of shapes as a regional integral, which avoids completely local differential computations involving contour points. This yields a boundary loss expressed with the regional softmax probability outputs of the network, which can be easily combined with standard regional losses and implemented with any existing deep network architecture for N-D segmentation.  We report comprehensive evaluations on two benchmark datasets corresponding to difficult, highly unbalanced problems: the ischemic stroke lesion (ISLES) and white matter hyperintensities (WMH). Used in conjunction with the region-based generalized Dice loss (GDL), our boundary loss improves performance significantly compared to GDL alone, reaching up to 8% improvement in Dice score and 10%  improvement in Hausdorff score. It also yielded a more stable learning process. Our code is publicly available.}
}


@inproceedings{Yang2019,
title={Major Vessel Segmentation on X-ray Coronary Angiography using Deep Networks with a Novel Penalty Loss Function},
author={Su Yang and Jihoon Kweon and Young-Hak Kim},
booktitle={International Conference on Medical Imaging with Deep Learning -- Extended Abstract Track},
address={London, UK},
year={2019},
month={08--10 Jul},
url={https://openreview.net/forum?id=H1lTh8unKN},
abstract={In  this  study,  we  proposed  a  segmentation  method  of  major  vessels  on  X-ray  coronary angiography using fully convolutional networks based on U-Net architecture.  A novel loss function pGD was  introduced  by  adding  a  term  for  penalizing  false  negative  and  false positive to generalized dice coefficient (GD).  DenseNet121 with pGD achieved the highest average DSC of 91.9{\ensuremath{\pm}}8.7{\%}, precision of 91.3{\ensuremath{\pm}}8.8{\%}, and recall of 92.6{\ensuremath{\pm}}9.6{\%}, respectively and showed improved segmentation performance compared to GD.}}

@article{Clough2019,
  title={A Topological Loss Function for Deep-Learning based Image Segmentation using Persistent Homology},
  author={James R. Clough and Ilkay {\"O}ks{\"u}z and Nicholas Byrne and Veronika A. Zimmer and Julia A. Schnabel and Andrew P. King},
  journal={ArXiv},
  year={2019},
  volume={abs/1910.01877}
}

@inproceedings{Mosinska2018_CVPR,
          author={Agata Mosinska and Pablo Márquez-Neila and Mateusz Kozinski and Pascal Fua},
          title={Beyond the Pixel-Wise Loss for Topology-Aware Delineation},
          year={2018},
          cdate={1514764800000},
          pages={3136-3145},
          url={http://openaccess.thecvf.com/content_cvpr_2018/html/Mosinska_Beyond_the_Pixel-Wise_CVPR_2018_paper.html},
          booktitle={CVPR}
        }
        


@article{clough_explicit_2019,
	title = {Explicit topological priors for deep-learning based image segmentation using persistent homology},
	url = {http://arxiv.org/abs/1901.10244},
	abstract = {We present a novel method to explicitly incorporate topological prior knowledge into deep learning based segmentation, which is, to our knowledge, the first work to do so. Our method uses the concept of persistent homology, a tool from topological data analysis, to capture high-level topological characteristics of segmentation results in a way which is differentiable with respect to the pixelwise probability of being assigned to a given class. The topological prior knowledge consists of the sequence of desired Betti numbers of the segmentation. As a proof-of-concept we demonstrate our approach by applying it to the problem of left-ventricle segmentation of cardiac MR images of 500 subjects from the UK Biobank dataset, where we show that it improves segmentation performance in terms of topological correctness without sacrificing pixelwise accuracy.},
	urldate = {2020-02-21},
	journal = {arXiv:1901.10244 [cs]},
	author = {Clough, James R. and Oksuz, Ilkay and Byrne, Nicholas and Schnabel, Julia A. and King, Andrew P.},
	month = jan,
	year = {2019},
	note = {arXiv: 1901.10244},
	keywords = {Computer Science - Computer Vision and Pattern Recognition}
}


@inproceedings{caliva2019_MIDL,
title={Distance Map Loss Penalty Term for Semantic Segmentation},
author={Francesco Caliva and Claudia Iriondo and Alejandro Morales Martinez and Sharmila Majumdar and Valentina Pedoia},
booktitle={International Conference on Medical Imaging with Deep Learning -- Extended Abstract Track},
address={London, UK},
year={2019},
month={08--10 Jul},
url={https://openreview.net/forum?id=B1eIcvS45V},
abstract={Convolutional neural networks for semantic segmentation suffer from low performance at object boundaries. In medical imaging, accurate representation of tissue surfaces and volumes is important for tracking of disease biomarkers such as tissue morphology and shape features.
In this work, we propose a novel distance map derived loss penalty term for semantic segmentation. We propose to use distance maps, derived from ground truth masks, to create a penalty term, guiding the network's focus towards hard-to-segment boundary regions. 
We investigate the effects of this penalizing factor against cross-entropy, Dice, and focal loss, among others, evaluating performance on a 3D MRI bone segmentation task from the publicly available Osteoarthritis Initiative dataset. We observe a significant improvement in the quality of segmentation, with better shape preservation at bone boundaries and areas affected by partial volume.
We ultimately aim to use our loss penalty term to improve the extraction of shape biomarkers and derive metrics to quantitatively evaluate the preservation of shape.},
}








@inproceedings{kervadec2020,
title={Bounding boxes for weakly supervised segmentation: Global constraints get close to full supervision},
author={Hoel Kervadec and Jose Dolz and Shanshan Wang and Eric Granger and Ismail ben Ayed},
booktitle={Medical Imaging with Deep Learning},
year={2020},
url={https://openreview.net/forum?id=VOQMC3rZtL}
}

@inproceedings{yang2020_MIDL,
title={Enhancing Foreground Boundaries for Medical Image Segmentation},
author={Dong Yang and Holger Roth and Xiaosong Wang and Ziyue Xu and Andriy Myronenko and Daguang Xu},
booktitle={Medical Imaging with Deep Learning},
year={2020},
url={https://openreview.net/forum?id=PlmI-C7dqI}
}
%%%%%%%%%%%%%%%%%%%%%%%ùothers%%%%%%%%%%%%%%
@inproceedings{Boykov2006,
author = {Boykov, Yuri and Kolmogorov, Vladimir and Cremers, Daniel and Delong, Andrew},
title = {An Integral Solution to Surface Evolution PDEs via Geo-Cuts},
year = {2006},
isbn = {3540338365},
publisher = {Springer-Verlag},
address = {Berlin, Heidelberg},
url = {https://doi.org/10.1007/11744078_32},
doi = {10.1007/11744078_32},
booktitle = {Proceedings of the 9th European Conference on Computer Vision - Volume Part III},
pages = {409–422},
numpages = {14},
location = {Graz, Austria},
series = {ECCV’06}
}


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%ùGraph based techniques for medical image segmentations %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
@article{Chen2018,
  title={A Survey of Graph Cuts/Graph Search Based Medical Image Segmentation},
  author={Xinjian Chen and Lingjiao Pan},
  journal={IEEE Reviews in Biomedical Engineering},
  year={2018},
  volume={11},
  pages={112-124}
}



@article{Mahapatra2017SemisupervisedLA,
  title={Semi-supervised learning and graph cuts for consensus based medical image segmentation},
  author={Dwarikanath Mahapatra},
  journal={Pattern Recognit.},
  year={2017},
  volume={63},
  pages={700-709}
}

@article{Kchichian2011EfficientMS,
  title={Efficient multi-object segmentation of 3D medical images using clustering and graph cuts},
  author={Razmig K{\'e}chichian and S{\'e}bastien Valette and Michel Desvignes and R{\'e}my Prost},
  journal={2011 18th IEEE International Conference on Image Processing},
  year={2011},
  pages={2149-2152}
}


@inproceedings{Sudre2017,
  title={Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations},
  author={Carole H. Sudre and Wenqi Li and Tom Vercauteren and S{\'e}bastien Ourselin and M. Jorge Cardoso},
  booktitle={DLMIA/ML-CDS@MICCAI},
  year={2017}
}





@misc{wu2016,
    title={Bridging Category-level and Instance-level Semantic Image Segmentation},
    author={Zifeng Wu and Chunhua Shen and Anton van den Hengel},
    year={2016},
    eprint={1605.06885},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

@inproceedings{Rahman2016,
  title={Optimizing Intersection-Over-Union in Deep Neural Networks for Image Segmentation},
  author={Md. Atiqur Rahman and Yuhuai Wang},
  booktitle={ISVC},
  year={2016}
}

@article{DEEPLab,
  author    = {Liang{-}Chieh Chen and
               George Papandreou and
               Iasonas Kokkinos and
               Kevin Murphy and
               Alan L. Yuille},
  title     = {DeepLab: Semantic Image Segmentation with Deep Convolutional Nets,
               Atrous Convolution, and Fully Connected CRFs},
  journal   = {CoRR},
  volume    = {abs/1606.00915},
  year      = {2016},
  url       = {http://arxiv.org/abs/1606.00915},
  archivePrefix = {arXiv},
  eprint    = {1606.00915},
  timestamp = {Mon, 13 Aug 2018 16:46:13 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/ChenPK0Y16.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{Baum2017,
  title={An Exploration of 2D and 3D Deep Learning Techniques for Cardiac MR Image Segmentation},
  author={Christian F. Baumgartner and Lisa M. Koch and Marc Pollefeys and Ender Konukoglu},
  booktitle={STACOM@MICCAI},
  year={2017}
}
@InProceedings{Yang2018,
author = {Yang, Xin and Bian, Cheng and Yu, Lequan and Ni, Dong and Heng, Pheng-Ann},
year = {2018},
month = {01},
pages = {152-160},
booktitle={STACOM@MICCAI},
title = {Class-Balanced Deep Neural Network for Automatic Ventricular Structure Segmentation},

}

@inproceedings{Jang2018,
title = "Automatic segmentation of LV and RV in cardiac MRI",
abstract = "Automatic and accurate segmentation of Left Ventricle (LV) and Right Ventricle (RV) in cine-MRI is required to analyze cardiac function and viability. We present a fully convolutional neural network to efficiently segment LV and RV as well as myocardium. The network is trained end-to-end from scratch. Average dice scores from five-fold cross-validation on the ACDC training dataset were 0.94, 0.89, and 0.88 for LV, RV, and myocardium. Experimental results show the robustness of the proposed architecture.",
author = "Yeonggul Jang and Yoonmi Hong and Seongmin Ha and Sekeun Kim and Chang, {Hyuk Jae}",
year = "2018",
month = jan,
day = "1",
doi = "10.1007/978-3-319-75541-0_17",
language = "English",
isbn = "9783319755403",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "161--169",
booktitle = "MICCAI workshop on Statistical Atlases and Computational Models of the Heart",
address = "Germany",
note = "8th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2017, Held in Conjunction with MICCAI 2017 ; Conference date: 10-09-2017 Through 14-09-2017",
}
@inproceedings{Carole_2017,
  title={Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations},
  author={Carole H. Sudre and Wenqi Li and Tom Vercauteren and S{\'e}bastien Ourselin and M. Jorge Cardoso},
  booktitle={DLMIA/ML-CDS@MICCAI},
  year={2017}
}

@article{TopK_Loss,
  title={Bridging Category-level and Instance-level Semantic Image Segmentation},
  author={Zifeng Wu and Chunhua Shen and Anton van den Hengel},
  journal={ArXiv},
  year={2016},
  volume={abs/1605.06885}
}



@inproceedings{Javier2019,
  author={Javier Ribera and David Guera and Yuhao Chen and Edward J. Delp},
  title={Locating Objects Without Bounding Boxes.},
  year={2019},
  cdate={1546300800000},
  pages={6479-6489},
  url={http://openaccess.thecvf.com/content_CVPR_2019/html/Ribera_Locating_Objects_Without_Bounding_Boxes_CVPR_2019_paper.html},
  booktitle={CVPR},
  crossref={conf/cvpr/2019}
}

@InProceedings{Drozdzal2016,
author="Drozdzal, Michal
and Vorontsov, Eugene
and Chartrand, Gabriel
and Kadoury, Samuel
and Pal, Chris",
editor="Carneiro, Gustavo
and Mateus, Diana
and Peter, Lo{\"i}c
and Bradley, Andrew
and Tavares, Jo{\~a}o Manuel R. S.
and Belagiannis, Vasileios
and Papa, Jo{\~a}o Paulo
and Nascimento, Jacinto C.
and Loog, Marco
and Lu, Zhi
and Cardoso, Jaime S.
and Cornebise, Julien",
title="The Importance of Skip Connections in Biomedical Image Segmentation",
booktitle="Deep Learning and Data Labeling for Medical Applications",
year="2016",
publisher="Springer International Publishing",
address="Cham",
pages="179--187",
abstract="In this paper, we study the influence of both long and short skip connections on Fully Convolutional Networks (FCN) for biomedical image segmentation. In standard FCNs, only long skip connections are used to skip features from the contracting path to the expanding path in order to recover spatial information lost during downsampling. We extend FCNs by adding short skip connections, that are similar to the ones introduced in residual networks, in order to build very deep FCNs (of hundreds of layers). A review of the gradient flow confirms that for a very deep FCN it is beneficial to have both long and short skip connections. Finally, we show that a very deep FCN can achieve near-to-state-of-the-art results on the EM dataset without any further post-processing.",
isbn="978-3-319-46976-8"
}



@InProceedings{Moeskops2016,
author="Moeskops, Pim
and Wolterink, Jelmer M.
and van der Velden, Bas H. M.
and Gilhuijs, Kenneth G. A.
and Leiner, Tim
and Viergever, Max A.
and I{\v{s}}gum, Ivana",
title="Deep Learning for Multi-task Medical Image Segmentation in Multiple Modalities",
booktitle="MICCAI",
year="2016",
pages="478--486",
abstract="Automatic segmentation of medical images is an important task for many clinical applications. In practice, a wide range of anatomical structures are visualised using different imaging modalities. In this paper, we investigate whether a single convolutional neural network (CNN) can be trained to perform different segmentation tasks.",
isbn="978-3-319-46723-8"
}



@ARTICLE{Crum2006,
  author={W. R. {Crum} and O. {Camara} and D. L. G. {Hill}},
  journal={IEEE Transactions on Medical Imaging}, 
  title={Generalized Overlap Measures for Evaluation and Validation in Medical Image Analysis}, 
  year={2006},
  volume={25},
  number={11},
  pages={1451-1461},}


@article{Taha2015,
  abstract = {},
  added-at = {2015-10-17T23:01:32.000+0200},
  author = {Taha, Abdel Aziz and Hanbury, Allan},
  biburl = {https://www.bibsonomy.org/bibtex/25de4c45dde21271f11bc0b8ed942df08/ataha},
  interhash = {5e1a2169d97771c1eb1bac43245f7984},
  intrahash = {5de4c45dde21271f11bc0b8ed942df08},
  journal = {BMC Medical Imaging},
  keywords = {ataha hanbury},
  month = aug,
  pages = 29,
  timestamp = {2015-10-17T23:08:45.000+0200},
  title = {Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool},
  url = {http://www.biomedcentral.com/1471-2342/15/29},
  volume = 15,
  year = 2015
}





@InProceedings{Fu2016_wce,
author="Fu, Huazhu
and Xu, Yanwu
and Lin, Stephen
and Kee Wong, Damon Wing
and Liu, Jiang",
title="DeepVessel: Retinal Vessel Segmentation via Deep Learning and Conditional Random Field",
booktitle="MICCAI",
year="2016",
pages="132--139",
abstract="Retinal vessel segmentation is a fundamental step for various ocular imaging applications. In this paper, we formulate the retinal vessel segmentation problem as a boundary detection task and solve it using a novel deep learning architecture. Our method is based on two key ideas: (1) applying a multi-scale and multi-level Convolutional Neural Network (CNN) with a side-output layer to learn a rich hierarchical representation, and (2) utilizing a Conditional Random Field (CRF) to model the long-range interactions between pixels. We combine the CNN and CRF layers into an integrated deep network called DeepVessel. Our experiments show that the DeepVessel system achieves state-of-the-art retinal vessel segmentation performance on the DRIVE, STARE, and CHASE{\_}DB1 datasets with an efficient running time.",
isbn="978-3-319-46723-8"
}


@InProceedings{Fu2016,
author="Fu, Huazhu
and Xu, Yanwu
and Lin, Stephen
and Kee Wong, Damon Wing
and Liu, Jiang",
title="DeepVessel: Retinal Vessel Segmentation via Deep Learning and Conditional Random Field",
booktitle="MICCAI",
year="2016",
pages="132--139",
abstract="Retinal vessel segmentation is a fundamental step for various ocular imaging applications. In this paper, we formulate the retinal vessel segmentation problem as a boundary detection task and solve it using a novel deep learning architecture. Our method is based on two key ideas: (1) applying a multi-scale and multi-level Convolutional Neural Network (CNN) with a side-output layer to learn a rich hierarchical representation, and (2) utilizing a Conditional Random Field (CRF) to model the long-range interactions between pixels. We combine the CNN and CRF layers into an integrated deep network called DeepVessel. Our experiments show that the DeepVessel system achieves state-of-the-art retinal vessel segmentation performance on the DRIVE, STARE, and CHASE{\_}DB1 datasets with an efficient running time.",
isbn="978-3-319-46723-8"
}


@inproceedings{Focalloss_2017,
  author={Tsung-Yi Lin and Priya Goyal and Ross B. Girshick and Kaiming He and Piotr Dollár},
  title={Focal Loss for Dense Object Detection},
  year={2017},
  cdate={1483228800000},
  pages={2999-3007},
  url={https://doi.org/10.1109/ICCV.2017.324},
  booktitle={ICCV},
  
}
% crossref={conf/iccv/2017}
@InProceedings{IoU_loss,
author="Rahman, Md Atiqur
and Wang, Yang",
editor="Bebis, George
and Boyle, Richard
and Parvin, Bahram
and Koracin, Darko
and Porikli, Fatih
and Skaff, Sandra
and Entezari, Alireza
and Min, Jianyuan
and Iwai, Daisuke
and Sadagic, Amela
and Scheidegger, Carlos
and Isenberg, Tobias",
title="Optimizing Intersection-Over-Union in Deep Neural Networks for Image Segmentation",
booktitle="Advances in Visual Computing",
year="2016",
publisher="Springer International Publishing",
address="Cham",
pages="234--244",
abstract="We consider the problem of learning deep neural networks (DNNs) for object category segmentation, where the goal is to label each pixel in an image as being part of a given object (foreground) or not (background). Deep neural networks are usually trained with simple loss functions (e.g., softmax loss). These loss functions are appropriate for standard classification problems where the performance is measured by the overall classification accuracy. For object category segmentation, the two classes (foreground and background) are very imbalanced. The intersection-over-union (IoU) is usually used to measure the performance of any object category segmentation method. In this paper, we propose an approach for directly optimizing this IoU measure in deep neural networks. Our experimental results on two object category segmentation datasets demonstrate that our approach outperforms DNNs trained with standard softmax loss.",
isbn="978-3-319-50835-1"
}

@inproceedings{Hoai2019,
title={Robustly segmenting quadriceps muscles of ultra-endurance athletes with weakly supervised U-Net},
author={Hoai-Thu Nguyen and Pierre Croisille and Magalie Viallon and Sarah Leclerc and Sylvain Grange and R{\'e}mi Grange and Olivier Bernard and Thomas Grenier},
booktitle={International Conference on Medical Imaging with Deep Learning -- Extended Abstract Track},
address={London, UK},
year={2019},
month={08--10 Jul},
url={https://openreview.net/forum?id=HJlVDNRV5E},
abstract={In this study, segmentation of quadriceps muscle heads of ultra-endurance athletes was done using a multi-atlas segmentation and corrective leaning framework where the registration based multi-atlas segmentation step was replaced with weakly supervised U-Net. For the case with remarkably different morphology, our method produced improved accuracy, while reduced signicantly the computation time.},
}
@InProceedings{SS_Loss2015,
author="Brosch, Tom
and Yoo, Youngjin
and Tang, Lisa Y. W.
and Li, David K. B.
and Traboulsee, Anthony
and Tam, Roger",
title="Deep Convolutional Encoder Networks for Multiple Sclerosis Lesion Segmentation",
booktitle="MICCAI",
year="2015",
pages="3--11",
abstract="We propose a novel segmentation approach based on deep convolutional encoder networks and apply it to the segmentation of multiple sclerosis (MS) lesions in magnetic resonance images. Our model is a neural network that has both convolutional and deconvolutional layers, and combines feature extraction and segmentation prediction in a single model. The joint training of the feature extraction and prediction layers allows the model to automatically learn features that are optimized for accuracy for any given combination of image types. In contrast to existing automatic feature learning approaches, which are typically patch-based, our model learns features from entire images, which eliminates patch selection and redundant calculations at the overlap of neighboring patches and thereby speeds up the training. Our network also uses a novel objective function that works well for segmenting underrepresented classes, such as MS lesions. We have evaluated our method on the publicly available labeled cases from the MS lesion segmentation challenge 2008 data set, showing that our method performs comparably to the state-of-theart. In addition, we have evaluated our method on the images of 500 subjects from an MS clinical trial and varied the number of training samples from 5 to 250 to show that the segmentation performance can be greatly improved by having a representative data set.",
isbn="978-3-319-24574-4"
}


@ARTICLE{Sun2020,
  author={L. {Sun} and W. {Ma} and X. {Ding} and Y. {Huang} and D. {Liang} and J. {Paisley}},
  journal={IEEE Transactions on Medical Imaging}, 
  title={A 3D Spatially Weighted Network for Segmentation of Brain Tissue From MRI}, 
  year={2020},
  volume={39},
  number={4},
  pages={898-909},}

@INPROCEEDINGS{ResNet-DUC-HDC,
  author={P. {Wang} and P. {Chen} and Y. {Yuan} and D. {Liu} and Z. {Huang} and X. {Hou} and G. {Cottrell}},
  booktitle={2018 IEEE Winter Conference on Applications of Computer Vision (WACV)}, 
  title={Understanding Convolution for Semantic Segmentation}, 
  year={2018},
  volume={},
  number={},
  pages={1451-1460},}
 @INPROCEEDINGS{Hu2019b,
  author={H. {Hu} and Y. {Zheng} and Q. {Zhou} and J. {Xiao} and S. {Chen} and Q. {Guan}},
  booktitle={2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)}, 
  title={MC-Unet: Multi-scale Convolution Unet for Bladder Cancer Cell Segmentation in Phase-Contrast Microscopy Images}, 
  year={2019},
  volume={},
  number={},
  pages={1197-1199},}
 @ARTICLE{Weng2019,
  author={Y. {Weng} and T. {Zhou} and Y. {Li} and X. {Qiu}},
  journal={IEEE Access}, 
  title={NAS-Unet: Neural Architecture Search for Medical Image Segmentation}, 
  year={2019},
  volume={7},
  number={},
  pages={44247-44257},}
  
  
@ARTICLE{Hu2019,
  author={J. {Hu} and H. {Wang} and S. {Gao} and M. {Bao} and T. {Liu} and Y. {Wang} and J. {Zhang}},
  journal={IEEE Access}, 
  title={S-UNet: A Bridge-Style U-Net Framework With a Saliency Mechanism for Retinal Vessel Segmentation}, 
  year={2019},
  volume={7},
  number={},
  pages={174167-174177},}
@INPROCEEDINGS{Chang2018,
  author={J. {Chang} and X. {Zhang} and J. {Chang} and M. {Ye} and D. {Huang} and P. {Wang} and C. {Yao}},
  booktitle={2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)}, 
  title={Brain Tumor Segmentation Based on 3D Unet with Multi-Class Focal Loss}, 
  year={2018},
  volume={},
  number={},
  pages={1-5},}

@INPROCEEDINGS{Zyuzin2019,
  author={V. {Zyuzin} and T. {Chumarnaya}},
  booktitle={2019 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT)}, 
  title={Comparison of Unet architectures for segmentation of the left ventricle endocardial border on two-dimensional ultrasound images}, 
  year={2019},
  volume={},
  number={},
  pages={110-113},}


@inproceedings{Roy2017,
  title={Error Corrective Boosting for Learning Fully Convolutional Networks with Limited Data},
  author={Abhijit Guha Roy and Sailesh Conjeti and Debdoot Sheet and Amin Katouzian and Nassir Navab and Christian Wachinger},
  booktitle={MICCAI},
  year={2017}
}

%   ##########################Morphological Loss  #########################


﻿@Article{Parvati2009,
author={Parvati, K.
and Prakasa Rao, B. S.
and Mariya Das, M.},
title={Image Segmentation Using Gray-Scale Morphology and Marker-Controlled Watershed Transformation},
journal={Discrete Dynamics in Nature and Society},
year={2009},
month={Jan},
day={05},
publisher={Hindawi Publishing Corporation},
volume={2008},
pages={384346},
abstract={Segmentation, a new method, for color, gray-scale MR medical images, and aerial images, is proposed. The method is based on gray-scale morphology. Edge detection algorithm includes function edge and marker-controlled watershed segmentation. It features the simple algorithm implemented in MATLAB. The watershed segmentation has been proved to be a powerful and fast technique for both contour detection and region-based segmentation. In principle, watershed segmentation depends on ridges to perform a proper segmentation, a property that is often fulfilled in contour detection where the boundaries of the objects are expressed as ridges. For region-based segmentation, it is possible to convert the edges of the objects into ridges by calculating an edge map of the image. Watershed is normally implemented by region growing, based on a set of markers to avoid oversegmentation.},
issn={1026-0226},
doi={10.1155/2008/384346},
url={https://doi.org/10.1155/2008/384346}
}


@article{Karimi2019ReducingTH,
  title={Reducing the Hausdorff Distance in Medical Image Segmentation With Convolutional Neural Networks},
  author={Davood Karimi and Septimiu E. Salcudean},
  journal={IEEE Transactions on Medical Imaging},
  year={2019},
  volume={39},
  pages={499-513}
}



@INPROCEEDINGS{Han2015,
  author={B. {Han}},
  booktitle={2015 2nd International Conference on Information Science and Control Engineering}, 
  title={Watershed Segmentation Algorithm Based on Morphological Gradient Reconstruction}, 
  year={2015},
  volume={},
  number={},
  pages={533-536},}

@INPROCEEDINGS {Yanlei2008,
author = {X. Yanlei and Z. Jiyin and J. Yubin},
booktitle = {Computer Science and Software Engineering, International Conference on},
title = {CT Image Segmentation Based on Order Morphological Gradient Map and Improved Region Growing Arithmetic},
year = {2008},
volume = {2},
issn = {},
pages = {1020-1023},
keywords = {medical image segmentation;region growing;floating threshold value},
doi = {10.1109/CSSE.2008.902},
url = {https://doi.ieeecomputersociety.org/10.1109/CSSE.2008.902},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
month = {dec}
}



@Inbook{Segonne2015,
author="S{\'e}gonne, F.
and Fischl, B.",
editor="Paragios, Nikos
and Duncan, James
and Ayache, Nicholas",
title="Integration of Topological Constraints in Medical Image Segmentation",
booktitle="Handbook of Biomedical Imaging: Methodologies and Clinical Research",
year="2015",
publisher="Springer US",
pages="245--262",
abstract="Topology is a strong global constraint that can be useful in generating geometrically accurate segmentations of anatomical structures. Conversely, topological ``defects'' or departures from the true topology of a structure due to segmentation errors can greatly reduce the utility of anatomical models. In this chapter we cover methods for integrating topological constraints into segmentation procedures in order to generate geometrically accurate and topologically correct models, which is critical for many clinical and research applications.",
isbn="978-0-387-09749-7",
doi="10.1007/978-0-387-09749-7_13",
url="https://doi.org/10.1007/978-0-387-09749-7_13"
}




@Article{Chu1998,
author={Chu, P. C.
and Beasley, J. E.},
title={A Genetic Algorithm for the Multidimensional Knapsack Problem},
journal={Journal of Heuristics},
year={1998},
month={Jun},
day={01},
volume={4},
number={1},
pages={63-86},
abstract={In this paper we present a heuristic based upon genetic algorithms for the multidimensional knapsack problem. A heuristic operator which utilises problem-specific knowledge is incorporated into the standard genetic algorithm approach. Computational results show that the genetic algorithm heuristic is capable of obtaining high-quality solutions for problems of various characteristics, whilst requiring only a modest amount of computational effort. Computational results also show that the genetic algorithm heuristic gives superior quality solutions to a number of other heuristics.},
issn={1572-9397},
doi={10.1023/A:1009642405419},
url={https://doi.org/10.1023/A:1009642405419}
}


@ARTICLE{Boykov2001,
  author={Boykov, Yuri and Veksler, Olga and Zabih, Ramin},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
  title={Fast approximate energy minimization via graph cuts}, 
  year={2001},
  volume={23},
  number={11},
  pages={1222-1239},}


@inproceedings{GoogleLeNet,
  author={Christian Szegedy and Wei Liu and Yangqing Jia and Pierre Sermanet and Scott E. Reed and Dragomir Anguelov and Dumitru Erhan and Vincent Vanhoucke and Andrew Rabinovich},
  title={Going deeper with convolutions},
  year={2015},
  cdate={1420070400000},
  pages={1-9},
  url={https://doi.org/10.1109/CVPR.2015.7298594},
  booktitle={CVPR},
  crossref={conf/cvpr/2015}
}

@InProceedings{VGG16,
  author       = "Karen Simonyan and Andrew Zisserman",
  title        = "Very Deep Convolutional Networks for Large-Scale Image Recognition",
  booktitle    = "International Conference on Learning Representations",
  year         = "2015",
}

@incollection{AlexNET,
title = {ImageNet Classification with Deep Convolutional Neural Networks},
author = {Alex Krizhevsky and Sutskever, Ilya and Hinton, Geoffrey E},
booktitle = {Advances in Neural Information Processing Systems 25},
editor = {F. Pereira and C. J. C. Burges and L. Bottou and K. Q. Weinberger},
pages = {1097--1105},
year = {2012},
url = {http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf}
}




