@article{Pappuru:2019,author = "Rajeev K R Pappuru and Luísa Ribeiro and Conceição Lobo et al.", 
title = "Microaneurysm turnover is a predictor of diabetic retinopathy progression", 
journal = "British Journal of Ophthalmology",
volume="103",
pages = "222--226",
year="2019",
keywords = "diabetes"}

@article{Grauslund:2018,
author = "Jakob Grauslund and Nis Andersen and Jens Andresen et al.",
title =  "Evidence-based Danish guidelines for screening of diabetic retinopathy",
journal = "Acta Ophthalmology",
volume="96",
number="8",
pages = "763--769", 
year="2018",
DOI="10.1111/aos.13936"}

@article{Noergaard:2018, 
author = "MF Nørgaard and Jakob Grauslund",
title = "Automated Screening for Diabetic Retinopathy - A Systematic Review",
journal = "Ophthalmic Research",
volume = "60",
number = "1",
pages = "9-–17",
year = "2018",
DOI="https://doi.org/10.1159/000486284"}

@article{Nielsen:2018,
author = "Katrine B Nielsen and Mie L Lautrup and Jakob K H Andersen et al.", 
title = "Deep Learning–Based Algorithms in Screening of Diabetic Retinopathy: A Systematic Review of Diagnostic Performance",
journal = "Ophthalmology Retina",
volume = "3",
number = "4",
pages = "294--304",
year = "2019",
DOI = "https://doi.org/10.1016/j.oret.2018.10.014"}

@InProceedings{Ronnenberger:2015,
author="Ronneberger, Olaf
and Fischer, Philipp
and Brox, Thomas",
title="U-Net: Convolutional Networks for Biomedical Image Segmentation",
booktitle="Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015",
year="2015",
publisher="Springer International Publishing",
address="Cham",
pages="234--241",
DOI = "10.1007/978-3-319-24574-4_28",
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 localization. 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"
}
@article{Zhang:2018, author= "Z Zhang and Q Liu and Y Wang", title= "Road Extraction by Deep Residual U-Net",
journal = "IEEE Geoscience and Remote Sensing Letters",
volume = "15",
number = "5",
pages = "749 -- 753",
year = "2018",
DOI = "10.1109/LGRS.2018.2802944"}


@InProceedings{Drozdzal:2016,
author="Drozdzal, Michal
and Vorontsov, Eugene
and Chartrand, Gabriel
et al.",
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",
DOI = "10.1007/978-3-319-46976-8_19",
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"
}
@article{Wilkinson:2003,
title = "Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales",
journal = "Ophthalmology",
volume = "110",
number = "9",
pages = "1677 - 1682",
year = "2003",
issn = "0161-6420",
doi = "https://doi.org/10.1016/S0161-6420(03)00475-5",
url = "http://www.sciencedirect.com/science/article/pii/S0161642003004755",
author = "C.P Wilkinson and Frederick L Ferris and Ronald E Klein et al.",
abstract = "Purpose
To develop consensus regarding clinical disease severity classification systems for diabetic retinopathy and diabetic macular edema that can be used around the world, and to improve communication and coordination of care among physicians who care for patients with diabetes.
Design
Report regarding the development of clinical diabetic retinopathy disease severity scales.
Participants
A group of 31 individuals from 16 countries, representing comprehensive ophthalmology, retina subspecialties, endocrinology, and epidemiology.
Methods
An initial clinical classification system, based on the Early Treatment Diabetic Retinopathy Study and the Wisconsin Epidemiologic Study of Diabetic Retinopathy publications, was circulated to the group in advance of a workshop. Each member reviewed this using e-mail, and a modified Delphi system was used to stratify responses. At a later workshop, separate systems for diabetic retinopathy and macular edema were developed. These were then reevaluated by group members, and the modified Delphi system was again used to measure degrees of agreement.
Main outcome measures
Consensus regarding specific classification systems was achieved.
Results
A five-stage disease severity classification for diabetic retinopathy includes three stages of low risk, a fourth stage of severe nonproliferative retinopathy, and a fifth stage of proliferative retinopathy. Diabetic macular edema is classified as apparently present or apparently absent. If training and equipment allow the screener to make a valid decision, macular edema is further categorized as a function of its distance from the central macula.
Conclusions
There seems to be a genuine need for consistent international clinical classification systems for diabetic retinopathy and diabetic macular edema that are supported with solid evidence. The proposed clinical classification systems provide a means of appropriately categorizing diabetic retinopathy and macular edema. It is hoped that these systems will be valuable in improving both screening of individuals with diabetes and communication and discussion among individuals caring for these patients."
}

@article{Decenciere:2003,
author = {Decencière, Etienne and Cazuguel, Guy and Zhang, Xiwei et al.},
year = {2013},
month = {04},
pages = {196-203},
title = {TeleOphta: Machine learning and image processing methods for teleophthalmology},
volume = {34},
journal = {IRBM},
doi = {10.1016/j.irbm.2013.01.010}
}

@article{Messidor:2,
	author = {Etienne Decencière and Xiwei Zhang and Guy Cazuguel et al.},
	title = {FEEDBACK ON A PUBLICLY DISTRIBUTED IMAGE DATABASE: THE MESSIDOR DATABASE},
	journal = {Image Analysis and Stereology},
	volume = {33},
	number = {3},
	year = {2014},
	keywords = {diabetic retinopathy; image database; image processing; Messidor},
	abstract = {The Messidor database, which contains hundreds of eye fundus images, has been
	publicly distributed since 2008. It was created by the Messidor project in order to evaluate
	automatic lesion segmentation and diabetic retinopathy grading methods. Designing, producing
	and maintaining such a database entails significant costs. By publicly sharing it, one hopes
	to bring a valuable resource to the public research community. However, the real interest and
	benefit of the research community is not easy to quantify. We analyse here the feedback on
	the Messidor database, after more than 6 years of diffusion. This analysis should apply to
	other similar research databases.},
	issn = {1854-5165},	pages = {231--234},	doi = {10.5566/ias.1155},
	url = {https://www.ias-iss.org/ojs/IAS/article/view/1155}
}

@misc{Keras,
  title={Keras},
  author={Chollet, Fran\c{c}ois and others},
  year={2015},
  howpublished={\url{https://keras.io}},
}

@article{Abraham:2018,
  title={A Novel Focal Tversky Loss Function With Improved Attention U-Net for Lesion Segmentation},
  author={Nabila Abraham and Naimul Mefraz Khan},
  journal={2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)},
  year={2018},
  pages={683-687}
}

@INPROCEEDINGS{Lin:2018,
author="T. Lin and P. Goyal and R. Girshick et al.",
booktitle="2017 IEEE International Conference on Computer Vision (ICCV)",
title="Focal Loss for Dense Object Detection",
year="2017",
pages="2999--3007",
keywords="convolution;entropy;image classification;neural nets;object detection;dense object detection;two-stage approach;classifier;sparse set;candidate object locations;extreme foreground-background class imbalance;focal loss;object detectors;R-CNN;dense detectors training;cross entropy loss reshaping;Detectors;Training;Entropy;Object detection;Proposals;Robustness;Computer vision",
doi="10.1109/ICCV.2017.324",
ISSN="2380-7504",
month="Oct"}

@article{Sudre:2017,
   title={Generalised Dice Overlap as a Deep Learning Loss Function for Highly Unbalanced Segmentations},
   ISBN={9783319675589},
   ISSN={1611-3349},
   url={http://dx.doi.org/10.1007/978-3-319-67558-9_28},
   DOI={10.1007/978-3-319-67558-9_28},
   journal={Lecture Notes in Computer Science},
   publisher={Springer International Publishing},
   author={Sudre, Carole H. and Li, Wenqi and Vercauteren, Tom and et al.},
   year={2017},
   pages={240–248}
}
@article{MZhang:2019,
  author    = {Michael R. Zhang and
               James Lucas and
               Geoffrey E. Hinton and
               et al.},
  title     = {Lookahead Optimizer: k steps forward, 1 step back},
  journal   = {CoRR},
  volume    = {abs/1907.08610},
  year      = {2019},
  url       = {http://arxiv.org/abs/1907.08610},
  archivePrefix = {arXiv},
  eprint    = {1907.08610},
  timestamp = {Tue, 23 Jul 2019 10:54:22 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1907-08610},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@misc{Lookahead,
  title = {keras-lookahead},
  howpublished = {\url{https://pypi.org/project/keras-lookahead/}},
  note = {Accessed: 2020-01-09}
}

@misc{RAdam,
  title = {keras-rectified-adam},
  howpublished = {\url{https://pypi.org/project/keras-rectified-adam/}},
  note = {Accessed: 2020-01-09}
}
@misc{Liu:2019,
    title={On the Variance of the Adaptive Learning Rate and Beyond},
    author={Liyuan Liu and Haoming Jiang and Pengcheng He and Weizhu Chen and Xiaodong Liu and Jianfeng Gao and Jiawei Han},
    year={2019},
    eprint={1908.03265},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}
@article{DeFauw:2018,
   title={Clinically applicable deep learning for diagnosis and referral in retinal disease},
   ISBN={9783319675589},
   ISSN={1611-3349},
   DOI={10.1038/s41591-018-0107-6},
   journal={Nat Med},
   volume = {33},
   author={Jeffrey De Fauw and Joseph R. Ledsam and Bernardino Romera-Paredes and et al.},
   year={2018},
   pages={1342–1350}
}

@article{Orlando:2018,
title = "An ensemble deep learning based approach for red lesion detection in fundus images",
journal = "Computer Methods and Programs in Biomedicine",
volume = "153",
pages = "115 - 127",
year = "2018",
issn = "0169-2607",
doi = "https://doi.org/10.1016/j.cmpb.2017.10.017",
url = "http://www.sciencedirect.com/science/article/pii/S0169260717307897",
author = "José Ignacio Orlando and Elena Prokofyeva and Mariana del Fresno and Matthew B. Blaschko",
keywords = "Fundus images, Diabetic retinopathy, Red lesion detection, Deep learning",
abstract = "Background and objectives: Diabetic retinopathy (DR) is one of the leading causes of preventable blindness in the world. Its earliest sign are red lesions, a general term that groups both microaneurysms (MAs) and hemorrhages (HEs). In daily clinical practice, these lesions are manually detected by physicians using fundus photographs. However, this task is tedious and time consuming, and requires an intensive effort due to the small size of the lesions and their lack of contrast. Computer-assisted diagnosis of DR based on red lesion detection is being actively explored due to its improvement effects both in clinicians consistency and accuracy. Moreover, it provides comprehensive feedback that is easy to assess by the physicians. Several methods for detecting red lesions have been proposed in the literature, most of them based on characterizing lesion candidates using hand crafted features, and classifying them into true or false positive detections. Deep learning based approaches, by contrast, are scarce in this domain due to the high expense of annotating the lesions manually. Methods: In this paper we propose a novel method for red lesion detection based on combining both deep learned and domain knowledge. Features learned by a convolutional neural network (CNN) are augmented by incorporating hand crafted features. Such ensemble vector of descriptors is used afterwards to identify true lesion candidates using a Random Forest classifier. Results: We empirically observed that combining both sources of information significantly improve results with respect to using each approach separately. Furthermore, our method reported the highest performance on a per-lesion basis on DIARETDB1 and e-ophtha, and for screening and need for referral on MESSIDOR compared to a second human expert. Conclusions: Results highlight the fact that integrating manually engineered approaches with deep learned features is relevant to improve results when the networks are trained from lesion-level annotated data. An open source implementation of our system is publicly available at https://github.com/ignaciorlando/red-lesion-detection."
}

@article{Chudzik:2018,
title = "Microaneurysm detection using fully convolutional neural networks",
journal = "Computer Methods and Programs in Biomedicine",
volume = "158",
pages = "185 - 192",
year = "2018",
issn = "0169-2607",
doi = "https://doi.org/10.1016/j.cmpb.2018.02.016",
url = "http://www.sciencedirect.com/science/article/pii/S0169260717308544",
author = "Piotr Chudzik and Somshubra Majumdar and Francesco Calivá and Bashir Al-Diri and Andrew Hunter",
keywords = "Medical image analysis, Microaneurysm detection, Convolutional neural networks, Retinal fundus images",
abstract = "Backround and Objectives
Diabetic retinopathy is a microvascular complication of diabetes that can lead to sight loss if treated not early enough. Microaneurysms are the earliest clinical signs of diabetic retinopathy. This paper presents an automatic method for detecting microaneurysms in fundus photographies.
Methods
A novel patch-based fully convolutional neural network with batch normalization layers and Dice loss function is proposed. Compared to other methods that require up to five processing stages, it requires only three. Furthermore, to the best of the authors’ knowledge, this is the first paper that shows how to successfully transfer knowledge between datasets in the microaneurysm detection domain.
Results
The proposed method was evaluated using three publicly available and widely used datasets: E-Ophtha, DIARETDB1, and ROC. It achieved better results than state-of-the-art methods using the FROC metric. The proposed algorithm accomplished highest sensitivities for low false positive rates, which is particularly important for screening purposes.
Conclusions
Performance, simplicity, and robustness of the proposed method demonstrates its suitability for diabetic retinopathy screening applications."
}

@misc{kervadec:2018,
    title={Boundary loss for highly unbalanced segmentation},
    author={Hoel Kervadec and Jihene Bouchtiba and Christian Desrosiers and Éric Granger and Jose Dolz and Ismail Ben Ayed},
    year={2018},
    eprint={1812.07032},
    archivePrefix={arXiv},
    primaryClass={eess.IV}
}

@ARTICLE{Adadi:2018,
author={A. {Adadi} and M. {Berrada}},
journal={IEEE Access},
title={Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)},
year={2018},
volume={6},
number={},
pages={52138-52160},
keywords={artificial intelligence;AI-based systems;black-box nature;explainable AI;XAI;explainable artificial intelligence;fourth industrial revolution;Conferences;Machine learning;Market research;Prediction algorithms;Machine learning algorithms;Biological system modeling;Explainable artificial intelligence;interpretable machine learning;black-box models},
doi={10.1109/ACCESS.2018.2870052},
ISSN={2169-3536},
month={},}

@article{Cochran:1950,
    author = {Cochran, W. G.},
    title = "{THE COMPARISON OF PERCENTAGES IN MATCHED SAMPLES}",
    journal = {Biometrika},
    volume = {37},
    number = {3-4},
    pages = {256-266},
    year = {1950},
    month = {12},
    issn = {0006-3444},
    doi = {10.1093/biomet/37.3-4.256},
    url = {https://doi.org/10.1093/biomet/37.3-4.256},
    eprint = {https://academic.oup.com/biomet/article-pdf/37/3-4/256/421327/37-3-4-256.pdf},
}

@article{Mcnemar:1947,
  title={Note on the sampling error of the difference between correlated proportions or percentages},
  author={McNemar, Quinn},
  journal={Psychometrika},
  volume={12},
  number={2},
  pages={153--157},
  year={1947},
  publisher={Springer-Verlag}
}

@article{raschkas_2018_mlxtend,
  author       = {Sebastian Raschka},
  title        = {MLxtend: Providing machine learning and data science 
                  utilities and extensions to Python’s  
                  scientific computing stack},
  journal      = {The Journal of Open Source Software},
  volume       = {3},
  number       = {24},
  month        = apr,
  year         = 2018,
  publisher    = {The Open Journal},
  doi          = {10.21105/joss.00638},
  url          = {http://joss.theoj.org/papers/10.21105/joss.00638}
}

@ARTICLE{Niemeijer:2009,

  author={M. {Niemeijer} and B. {van Ginneken} and M. J. {Cree} and A. {Mizutani} and G. {Quellec} and C. I. {Sanchez} and B. {Zhang} and R. {Hornero} and M. {Lamard} and C. {Muramatsu} and X. {Wu} and G. {Cazuguel} and J. {You} and A. {Mayo} and Q. {Li} and Y. {Hatanaka} and B. {Cochener} and C. {Roux} and F. {Karray} and M. {Garcia} and H. {Fujita} and M. D. {Abramoff}},

  journal={IEEE Transactions on Medical Imaging}, 

  title={Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs}, 

  year={2010},

  volume={29},

  number={1},

  pages={185-195},}
  
@Manual{NLME,
    title = {{nlme}: Linear and Nonlinear Mixed Effects Models},
    author = {Jose Pinheiro and Douglas Bates and Saikat DebRoy and Deepayan Sarkar and {R Core Team}},
    year = {2020},
    note = {R package version 3.1-148},
    url = {https://CRAN.R-project.org/package=nlme},
  }
  
@Manual{EMMEANS,
    title = {emmeans: Estimated Marginal Means, aka Least-Squares Means},
    author = {Russell Lenth},
    year = {2020},
    note = {R package version 1.4.7},
    url = {https://CRAN.R-project.org/package=emmeans},
  }
  
  @article{Krause2017,
author = {Krause, Jonathan and Gulshan, Varun and Rahimy, Ehsan and Karth, Peter and Widner, Kasumi and Corrado, Greg and Peng, Lily and Webster, Dale},
year = {2017},
month = {10},
pages = {},
title = {Grader Variability and the Importance of Reference Standards for Evaluating Machine Learning Models for Diabetic Retinopathy},
volume = {125},
journal = {Ophthalmology},
doi = {10.1016/j.ophtha.2018.01.034}
}

@article{Savelli2020,
title = "A multi-context CNN ensemble for small lesion detection",
journal = "Artificial Intelligence in Medicine",
volume = "103",
pages = "101749",
year = "2020",
issn = "0933-3657",
doi = "https://doi.org/10.1016/j.artmed.2019.101749",
url = "http://www.sciencedirect.com/science/article/pii/S0933365719303082",
author = "B. Savelli and A. Bria and M. Molinara and C. Marrocco and F. Tortorella",
keywords = "Ensemble classifier, Deep learning, Convolutional neural networks, Computer-aided detection (CADe), Mammograms, Ocular fundus images",
abstract = "In this paper, we propose a novel method for the detection of small lesions in digital medical images. Our approach is based on a multi-context ensemble of convolutional neural networks (CNNs), aiming at learning different levels of image spatial context and improving detection performance. The main innovation behind the proposed method is the use of multiple-depth CNNs, individually trained on image patches of different dimensions and then combined together. In this way, the final ensemble is able to find and locate abnormalities on the images by exploiting both the local features and the surrounding context of a lesion. Experiments were focused on two well-known medical detection problems that have been recently faced with CNNs: microcalcification detection on full-field digital mammograms and microaneurysm detection on ocular fundus images. To this end, we used two publicly available datasets, INbreast and E-ophtha. Statistically significantly better detection performance were obtained by the proposed ensemble with respect to other approaches in the literature, demonstrating its effectiveness in the detection of small abnormalities."
}
