@book{Lamport:Book:1989,
 author = {Lamport, Leslie},
 title = {Latex: A Document Preparation System},
 year = {1986},
 isbn = {0-201-15790-X},
 publisher = {Addison-Wesley Longman Publishing Co., Inc.},
 address = {Boston, MA, USA},
}

tan2018fully@article{Acton2009,
author = {Acton, Scott T. and Ray, Nilanjan},
title = {Biomedical Image Analysis: Segmentation},
journal = {Synthesis Lectures on Image, Video, and Multimedia Processing},
volume = {4},
number = {1},
pages = {1-108},
year = {2009}
}

@INPROCEEDINGS{Ghosh2017, 
author={S. {Ghosh} and P. {Boulanger} and S. T. {Acton} and S. S. {Blemker} and N. {Ray}}, 
booktitle={2017 IEEE ICIP}, 
title={Automated 3D muscle segmentation from MRI data using convolutional neural network}, 
year={2017}, 
volume={}, 
number={}, 
pages={4437-4441}, 
keywords={biomedical MRI;image classification;image segmentation;medical image processing;muscle;neural nets;principal component analysis;ground truth segmentation;segmentation image;CNN model;MRI data;automated segmentation algorithm;human leg muscles;deep convolutional neural network;generalized cylinder model;3D human leg muscle;raw 3D voxels;AlexNet;PCA-head;3D leg muscle boundary;structured-output CNN architecture;principle components analysis;automated 3D muscle segmentation;AAM image segmentation algorithm;active appearance model;Dice score;compressed vector;Magnetic resonance imaging (MRI);leg muscle segmentation;3D modeling;principal component analysis (PCA);convolutional neural networks (CNN)}, 
doi={10.1109/ICIP.2017.8297121}, 
ISSN={2381-8549}, 
month={Sep.},}

@ARTICLE{Lesion_seg,
author={T. Brosch and L. Y. W. Tang and Y. Yoo and D. K. B. Li and A. Traboulsee and R. Tam},
journal={IEEE Trans. Med. Im.},
title={Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation},
year={2016},
volume={35},
number={5},
pages={1229-1239},
doi={10.1109/TMI.2016.2528821},
ISSN={0278-0062},
month={May},}

@ARTICLE{Tumor_seg,
author={S. Pereira and A. Pinto and V. Alves and C. A. Silva},
journal={IEEE Transactions on Medical Imaging},
title={Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images},
year={2016},
volume={35},
number={5},
pages={1240-1251},
doi={10.1109/TMI.2016.2538465},
ISSN={0278-0062},
month={May},}

@article{Cell_tracking,
title = "Cell tracking using deep neural networks with multi-task learning",
journal = "Image and Vision Computing",
volume = "60",
pages = "142 - 153",
year = "2017",
issn = "0262-8856",
author = "Tao He and Hua Mao and Jixiang Guo and Zhang Yi"
}

@article{GHOSAL201781,
title = "Deep deformable registration: Enhancing accuracy by fully convolutional neural net",
journal = "Pattern Recognition Letters",
volume = "94",
pages = "81 - 86",
year = "2017",
issn = "0167-8655",
doi = "https://doi.org/10.1016/j.patrec.2017.05.022",
author = "Sayan Ghosal and Nilanjan Ray"
}

@ARTICLE{Deep_detection,
author={Q. Dou and H. Chen and L. Yu and L. Zhao and J. Qin and D. Wang and V. C. Mok and L. Shi and P. Heng},
journal={IEEE TMI},
title={Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks},
year={2016},
volume={35},
number={5},
pages={1182-1195},
ISSN={0278-0062},
month={May},}

@ARTICLE{Greenspan,
author={H. Greenspan and B. van Ginneken and R. M. Summers},
journal={IEEE Transactions on Medical Imaging},
title={Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique},
year={2016},
volume={35},
number={5},
pages={1153-1159},
ISSN={0278-0062},
month={May},}

@article{Interpretability,
title = "Improving the radiologist–CAD interaction: designing for appropriate trust",
journal = "Clinical Radiology",
volume = "70",
number = "2",
pages = "115 - 122",
year = "2015",
issn = "0009-9260",
doi = "https://doi.org/10.1016/j.crad.2014.09.017",
author = "W. Jorritsma and F. Cnossen and P.M.A. van Ooijen"
}

@article{Shen2018AnID,
  title={An Interpretable Deep Hierarchical Semantic Convolutional Neural Network for Lung Nodule Malignancy Classification},
  author={Shiwen Shen and Simon X. Han and Denise R. Aberle and Alex A. T. Bui and William Hsu},
  journal={CoRR},
  year={2018},
  volume={abs/1806.00712}
}

@ARTICLE{ES2017,
   author = {{Salimans}, T. and {Ho}, J. and {Chen}, X. and {Sidor}, S. and 
	{Sutskever}, I.},
    title = "{Evolution Strategies as a Scalable Alternative to Reinforcement Learning}",
  journal = {ArXiv e-prints},
archivePrefix = "arXiv",
   eprint = {1703.03864},
 primaryClass = "stat.ML",
     year = 2017,
    month = mar
}

@article{SG2016,
  author    = {Max Jaderberg and
               Wojciech Marian Czarnecki and
               Simon Osindero and
               Oriol Vinyals and
               Alex Graves and
               Koray Kavukcuoglu},
  title     = {Decoupled Neural Interfaces using Synthetic Gradients},
  journal   = {CoRR},
  volume    = {abs/1608.05343},
  year      = {2016},
  archivePrefix = {arXiv},
  eprint    = {1608.05343},
  timestamp = {Mon, 13 Aug 2018 16:46:21 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/JaderbergCOVGK16},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@InProceedings{LV_seg,
author="Baumgartner, Christian F.
and Koch, Lisa M.
and Pollefeys, Marc
and Konukoglu, Ender",
editor="Pop, Mihaela
and others",
title="An Exploration of 2D and 3D Deep Learning Techniques for Cardiac MR Image Segmentation",
booktitle="Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges",
year="2018",
publisher="Springer Int. Publishing",
address="Cham",
pages="111--119",
isbn="978-3-319-75541-0"
}

@ARTICLE{Unet,
   author = {{Ronneberger}, O. and {Fischer}, P. and {Brox}, T.},
    title = "{U-Net: Convolutional Networks for Biomedical Image Segmentation}",
  journal = {ArXiv e-prints},
archivePrefix = "arXiv",
   eprint = {1505.04597},
 primaryClass = "cs.CV",
 keywords = {Computer Science - Computer Vision and Pattern Recognition},
     year = 2015,
    month = may,
   adsurl = {http://adsabs.harvard.edu/abs/2015arXiv150504597R},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@ARTICLE{Soft_assn,
   author = {{Bahdanau}, D. and {Cho}, K. and {Bengio}, Y.},
    title = "{Neural Machine Translation by Jointly Learning to Align and Translate}",
  journal = {ArXiv e-prints},
archivePrefix = "arXiv",
   eprint = {1409.0473},
 primaryClass = "cs.CL",
     year = 2014,
    month = sep,
   adsurl = {http://adsabs.harvard.edu/abs/2014arXiv1409.0473B},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@article{Baydin,
  author  = {Atilim Gunes Baydin and Barak A. Pearlmutter and Alexey Andreyevich Radul and Jeffrey Mark Siskind},
  title   = {Automatic Differentiation in Machine Learning: a Survey},
  journal = {J. Mac. Learn. Res.},
  year    = {2018},
  volume  = {18},
  number  = {153},
  pages   = {1-43}
}

@INPROCEEDINGS{DPSnake,
author={N. Ray and S. T. Acton and H. Zhang},
booktitle={Proceedings of the 21st International Conf. on Patt. Rec. (ICPR2012)},
title={Seeing through clutter: Snake computation with dynamic programming for particle segmentation},
year={2012},
volume={},
number={},
pages={801-804},
doi={},
ISSN={1051-4651},
month={Nov},}

@article{retinopathy,
author = {Gulshan V and Peng L and Coram M and et al},
title = {Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs},
journal = {JAMA},
volume = {316},
number = {22},
pages = {2402-2410},
year = {2016}
}

@ARTICLE{DL_MIA,
author={J. Ker and L. Wang and J. Rao and T. Lim},
journal={IEEE Access},
title={Deep Learning Applications in Medical Image Analysis},
year={2018},
volume={6},
number={},
pages={9375-9389},
ISSN={2169-3536},
month={},}

@ARTICLE{Transfer,
   author = {{Shin}, H.-C. and others},
    title = "{Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning}",
  journal = {ArXiv e-prints},
archivePrefix = "arXiv",
   eprint = {1602.03409},
 primaryClass = "cs.CV",
 keywords = {Computer Science - Computer Vision and Pattern Recognition},
     year = 2016,
    month = feb,
   adsurl = {http://adsabs.harvard.edu/abs/2016arXiv160203409S},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@ARTICLE{Ravishankar,
   author = {{Ravishankar}, H. and {Sudhakar}, P. and {Venkataramani}, R. and 
	{Thiruvenkadam}, S. and {Annangi}, P. and {Babu}, N. and {Vaidya}, V.
	},
    title = "{Understanding the Mechanisms of Deep Transfer Learning for Medical Images}",
  journal = {ArXiv e-prints},
archivePrefix = "arXiv",
   eprint = {1704.06040},
 primaryClass = "cs.CV",
 keywords = {Computer Science - Computer Vision and Pattern Recognition},
     year = 2017,
    month = apr,
   adsurl = {http://adsabs.harvard.edu/abs/2017arXiv170406040R},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@ARTICLE{Tajbakhsh,
author={N. Tajbakhsh and J. Y. Shin and S. R. Gurudu and R. T. Hurst and C. B. Kendall and M. B. Gotway and J. Liang},
journal={IEEE TMI},
title={Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?},
year={2016},
volume={35},
number={5},
pages={1299-1312},
ISSN={0278-0062},
month={May},}

@ARTICLE{Yao,
   author = {{Xue}, Y. and {Bigras}, G. and {Hugh}, J. and {Ray}, N.},
    title = "{Training Convolutional Neural Networks and Compressed Sensing End-to-End for Microscopy Cell Detection}",
  journal = {ArXiv e-prints},
archivePrefix = "arXiv",
   eprint = {1810.03075},
 primaryClass = "cs.CV",
 keywords = {Computer Science - Computer Vision and Pattern Recognition},
     year = 2018,
    month = oct,
   adsurl = {http://adsabs.harvard.edu/abs/2018arXiv181003075X},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@ARTICLE{Felzenszwalb,
author={P. F. Felzenszwalb and R. Zabih},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
title={Dynamic Programming and Graph Algorithms in Computer Vision},
year={2011},
volume={33},
number={4},
pages={721-740},
doi={10.1109/TPAMI.2010.135},
ISSN={0162-8828},
month={April},}

@INPROCEEDINGS{DeepLevelset,
author={P. Hu and B. Shuai and J. Liu and G. Wang},
booktitle={2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
title={Deep Level Sets for Salient Object Detection},
year={2017},
volume={},
number={},
pages={540-549},
doi={10.1109/CVPR.2017.65},
ISSN={1063-6919},
month={July},}

@article{TangVZCJ17,
  author    = {Min Tang and
               Sepehr Valipour and
               Zichen Vincent Zhang and
               Dana Cobzas and
               Martin J{\"{a}}gersand},
  title     = {A deep level set method for image segmentation},
  journal   = {CoRR},
  volume    = {abs/1705.06260},
  year      = {2017},
  url       = {http://arxiv.org/abs/1705.06260},
  archivePrefix = {arXiv},
  eprint    = {1705.06260},
  timestamp = {Mon, 13 Aug 2018 16:48:53 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/TangVZCJ17},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{NGO2017159,
title = "Combining deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance",
journal = "Medical Image Analysis",
volume = "35",
pages = "159 - 171",
year = "2017",
issn = "1361-8415",
doi = "https://doi.org/10.1016/j.media.2016.05.009",
author = "Tuan Anh Ngo and Zhi Lu and Gustavo Carneiro"
}

@ARTICLE{LevelsetRNN,
author={T. H. N. Le and K. G. Quach and K. Luu and C. N. Duong and M. Savvides},
journal={IEEE Transactions on Image Processing},
title={Reformulating Level Sets as Deep Recurrent Neural Network Approach to Semantic Segmentation},
year={2018},
volume={27},
number={5},
pages={2393-2407},
ISSN={1057-7149},
month={May},}

@article{DeepAC,
  author    = {Christian Rupprecht and
               Elizabeth Huaroc and
               Maximilian Baust and
               Nassir Navab},
  title     = {Deep Active Contours},
  journal   = {CoRR},
  volume    = {abs/1607.05074},
  year      = {2016},
  url       = {http://arxiv.org/abs/1607.05074},
  archivePrefix = {arXiv},
  eprint    = {1607.05074},
  timestamp = {Mon, 13 Aug 2018 16:47:36 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/RupprechtHBN16},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@ARTICLE{ShapePriorCNN,
author={C. Zotti and Z. Luo and A. Lalande and P. Jodoin},
journal={IEEE J. of Biom. and Health Informatics},
title={Convolutional Neural Network with Shape Prior Applied to Cardiac MRI Segmentation},
year={2018},
volume={},
number={},
pages={1-1},
doi={10.1109/JBHI.2018.2865450},
ISSN={2168-2194},
month={},}

@article{Acton,
author = {Acton, Scott T. and Ray, Nilanjan},
title = {Biomedical Image Analysis: Segmentation},
journal = {Synthesis Lectures on Image, Video, and Multimedia Processing},
volume = {4},
number = {1},
pages = {1-108},
year = {2009},
}

@ARTICLE{EndToEndAC,
   author = {{Marcos}, D. and {Tuia}, D. and {Kellenberger}, B. and {Zhang}, L. and 
	{Bai}, M. and {Liao}, R. and {Urtasun}, R.},
    title = "{Learning deep structured active contours end-to-end}",
  journal = {ArXiv e-prints},
archivePrefix = "arXiv",
   eprint = {1803.06329},
 primaryClass = "cs.CV",
 keywords = {Computer Science - Computer Vision and Pattern Recognition},
     year = 2018,
    month = mar,
   adsurl = {http://adsabs.harvard.edu/abs/2018arXiv180306329M},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@article{bernard2018deep,
  title={Deep Learning Techniques for Automatic MRI Cardiac Multi-structures Segmentation and Diagnosis: Is the Problem Solved?},
  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 Ballester, Miguel Angel Gonzalez and others},
  journal={IEEE Trans. on Medical Imaging},
  year={2018},
  publisher={IEEE}
}

@article{kingma2014adam,
  title={Adam: A method for stochastic optimization},
  author={Kingma, Diederik P and Ba, Jimmy},
  journal={arXiv preprint arXiv:1412.6980},
  year={2014}
}

@article{Glasmachers17,
  author    = {Tobias Glasmachers},
  title     = {Limits of End-to-End Learning},
  journal   = {CoRR},
  volume    = {abs/1704.08305},
  year      = {2017},
  url       = {http://arxiv.org/abs/1704.08305},
  archivePrefix = {arXiv},
  eprint    = {1704.08305},
  timestamp = {Mon, 13 Aug 2018 16:46:33 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/Glasmachers17},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{Hornik1990UniversalAO,
  title={Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks},
  author={Kurt Hornik and Maxwell B. Stinchcombe and Halbert White},
  journal={Neural Networks},
  year={1990},
  volume={3},
  pages={551-560}
}

@InProceedings{ASSD,
author="Gerig, Guido
and Jomier, Matthieu
and Chakos, Miranda",
editor="Niessen, Wiro J.
and Viergever, Max A.",
title="Valmet: A New Validation Tool for Assessing and Improving 3D Object Segmentation",
booktitle="Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2001",
year="2001",
publisher="Springer Berlin Heidelberg",
address="Berlin, Heidelberg",
pages="516--523",
isbn="978-3-540-45468-7"
}

@article{xue2018full,
  title={Full left ventricle quantification via deep multitask relationships learning},
  author={Xue, Wufeng and Brahm, Gary and Pandey, Sachin and Leung, Stephanie and Li, Shuo},
  journal={MIA},
  volume={43},
  pages={54--65},
  year={2018},
  publisher={Elsevier}
}

@article{tan2018fully,
  title={Fully automated segmentation of the left ventricle in cine cardiac MRI using neural network regression},
  author={Tan, Li Kuo and McLaughlin, Robert A and Lim, Einly and Abdul Aziz, Yang Faridah and Liew, Yih Miin},
  journal={J. of Magnetic Resonance Imaging},
  volume={48},
  number={1},
  pages={140--152},
  year={2018},
  publisher={Wiley Online Library}
}

@article{tan2017,
  title={Convolutional neural network regression for short-axis left ventricle segmentation in cardiac cine MR sequences},
  author={Tan, Li Kuo and and Liew, Yih Miin and McLaughlin, Robert A},
  journal={Medical image analysis},
  volume={39},
  pages={78--86},
  year={2017}
}

@article{Santiago2018,
  title={Fast segmentation of the left ventricle in cardiac MRI using dynamic programming},
  author={Santiago, C. and Nascimento, J. C. and Marques, J. S},
  journal={Computer methods and programs in biomedicine},
  volume={154},
  pages={9--23},
  year={2018}
}

@inproceedings{Poincare18,
  author    = {Yuanhan Mo and
               Fangde Liu and
               Douglas McIlwraith and
               Guang Yang and
               Jingqing Zhang and
               Taigang He and
               Yike Guo},
  title     = {The Deep Poincar{\'{e}} Map: {A} Novel Approach for Left Ventricle
               Segmentation},
  booktitle = {MICCAI
               2018 - Proceedings, Part {IV}},
  pages     = {561--568},
  year      = {2018},
  timestamp = {Tue, 14 May 2019 10:00:50 +0200},
  biburl    = {https://dblp.org/rec/bib/conf/miccai/MoLMYZHG18},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@ARTICLE{Luo2018,
author={G. {Luo} and S. {Dong} and K. {Wang} and W. {Zuo} and S. {Cao} and H. {Zhang}},
journal={IEEE Transactions on Biomedical Engineering},
title={Multi-Views Fusion CNN for Left Ventricular Volumes Estimation on Cardiac MR Images},
year={2018},
volume={65},
number={9},
pages={1924-1934},
keywords={biomedical MRI;cardiology;convolution;diseases;feedforward neural nets;medical image processing;end-to-end LV volume prediction method;data preprocessing method;cardiac magnetic resonance;cardiac disease diagnosis;end-to-end deep convolutional neural networks;cardiac MR images;left ventricular volumes estimation;multiviews fusion CNN;CMR;Volume measurement;Image segmentation;Neural networks;Data preprocessing;Cardiac disease;Benchmark testing;CMR;deep convolutional neural networks;left ventricle;volumes estimation},
ISSN={0018-9294},
month={Sep.},}

@ARTICLE{Zheng2018,
author={Q. {Zheng} and H. {Delingette} and N. {Duchateau} and N. {Ayache}},
journal={IEEE Transactions on Medical Imaging},
title={3-D Consistent and Robust Segmentation of Cardiac Images by Deep Learning With Spatial Propagation},
year={2018},
volume={37},
number={9},
pages={2137-2148},
keywords={biomedical MRI;cardiology;image segmentation;learning (artificial intelligence);medical image processing;U.K. Biobank;state-of-the-art cohorts;enhanced spatial consistency;unseen cases;robust segmentation;cardiac images;deep learning;spatial propagation;cardiac segmentation;short axis Magnetic resonance;bottom slice;iteration;adjacent slice;existing segmentation;3D consistent segmentation;U;Image segmentation;Three-dimensional displays;Training;Machine learning;Two dimensional displays;Magnetic resonance imaging;Neural networks;Cardiac segmentation;deep learning;neural network;3D consistency;spatial propagation},
ISSN={0278-0062},
month={Sep.},}