
@InProceedings{pmlr-v48-gal16,
  title = 	 {Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning},
  author = 	 {Gal, Yarin and Ghahramani, Zoubin},
  booktitle = 	 {Proceedings of The 33rd International Conference on Machine Learning},
  pages = 	 {1050--1059},
  year = 	 {2016},
  editor = 	 {Balcan, Maria Florina and Weinberger, Kilian Q.},
  volume = 	 {48},
  series = 	 {Proceedings of Machine Learning Research},
  address = 	 {New York, New York, USA},
  month = 	 {20--22 Jun},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v48/gal16.pdf},
  url = 	 {https://proceedings.mlr.press/v48/gal16.html},
  abstract = 	 {Deep learning tools have gained tremendous attention in applied machine learning. However such tools for regression and classification do not capture model uncertainty. In comparison, Bayesian models offer a mathematically grounded framework to reason about model uncertainty, but usually come with a prohibitive computational cost. In this paper we develop a new theoretical framework casting dropout training in deep neural networks (NNs) as approximate Bayesian inference in deep Gaussian processes. A direct result of this theory gives us tools to model uncertainty with dropout NNs – extracting information from existing models that has been thrown away so far. This mitigates the problem of representing uncertainty in deep learning without sacrificing either computational complexity or test accuracy. We perform an extensive study of the properties of dropout’s uncertainty. Various network architectures and non-linearities are assessed on tasks of regression and classification, using MNIST as an example. We show a considerable improvement in predictive log-likelihood and RMSE compared to existing state-of-the-art methods, and finish by using dropout’s uncertainty in deep reinforcement learning.}
}

@inproceedings{NEURIPS2018_473447ac,
 author = {Kohl, Simon and Romera-Paredes, Bernardino and Meyer, Clemens and De Fauw, Jeffrey and Ledsam, Joseph R. and Maier-Hein, Klaus and Eslami, S. M. Ali and Jimenez Rezende, Danilo and Ronneberger, Olaf},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
 pages = {},
 publisher = {Curran Associates, Inc.},
 title = {A Probabilistic U-Net for Segmentation of Ambiguous Images},
 url = {https://proceedings.neurips.cc/paper_files/paper/2018/file/473447ac58e1cd7e96172575f48dca3b-Paper.pdf},
 volume = {31},
 year = {2018}
}

@Article{app9153064,
AUTHOR = {Kim, Mijung and Han, Jong Chul and Hyun, Seung Hyup and Janssens, Olivier and Van Hoecke, Sofie and Kee, Changwon and De Neve, Wesley},
TITLE = {Medinoid: Computer-Aided Diagnosis and Localization of Glaucoma Using Deep Learning †},
JOURNAL = {Applied Sciences},
VOLUME = {9},
YEAR = {2019},
NUMBER = {15},
ARTICLE-NUMBER = {3064},
URL = {https://www.mdpi.com/2076-3417/9/15/3064},
ISSN = {2076-3417},
ABSTRACT = {Glaucoma is a leading eye disease, causing vision loss by gradually affecting peripheral vision if left untreated. Current diagnosis of glaucoma is performed by ophthalmologists, human experts who typically need to analyze different types of medical images generated by different types of medical equipment: fundus, Retinal Nerve Fiber Layer (RNFL), Optical Coherence Tomography (OCT) disc, OCT macula, perimetry, and/or perimetry deviation. Capturing and analyzing these medical images is labor intensive and time consuming. In this paper, we present a novel approach for glaucoma diagnosis and localization, only relying on fundus images that are analyzed by making use of state-of-the-art deep learning techniques. Specifically, our approach towards glaucoma diagnosis and localization leverages Convolutional Neural Networks (CNNs) and Gradient-weighted Class Activation Mapping (Grad-CAM), respectively. We built and evaluated different predictive models using a large set of fundus images, collected and labeled by ophthalmologists at Samsung Medical Center (SMC). Our experimental results demonstrate that our most effective predictive model is able to achieve a high diagnosis accuracy of 96%, as well as a high sensitivity of 96% and a high specificity of 100% for Dataset-Optic Disc (OD), a set of center-cropped fundus images highlighting the optic disc. Furthermore, we present Medinoid, a publicly-available prototype web application for computer-aided diagnosis and localization of glaucoma, integrating our most effective predictive model in its back-end.},
DOI = {10.3390/app9153064}
}

@Article{Liu2021,
author={Liu, Bingyan
and Pan, Daru
and Song, Hui},
title={Joint optic disc and cup segmentation based on densely connected depthwise separable convolution deep network},
journal={BMC Medical Imaging},
year={2021},
month={Jan},
day={28},
volume={21},
number={1},
pages={14},
abstract={Glaucoma is an eye disease that causes vision loss and even blindness. The cup to disc ratio (CDR) is an important indicator for glaucoma screening and diagnosis. Accurate segmentation for the optic disc and cup helps obtain CDR. Although many deep learning-based methods have been proposed to segment the disc and cup for fundus image, achieving highly accurate segmentation performance is still a great challenge due to the heavy overlap between the optic disc and cup.},
issn={1471-2342},
doi={10.1186/s12880-020-00528-6},
url={https://doi.org/10.1186/s12880-020-00528-6}
}

@InProceedings{phiseg,
author="Baumgartner, Christian F.
and Tezcan, Kerem C.
and Chaitanya, Krishna
and H{\"o}tker, Andreas M.
and Muehlematter, Urs J.
and Schawkat, Khoschy
and Becker, Anton S.
and Donati, Olivio
and Konukoglu, Ender",
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",
title="PHiSeg: Capturing Uncertainty in Medical Image Segmentation",
booktitle="Medical Image Computing and Computer Assisted Intervention -- MICCAI 2019",
year="2019",
publisher="Springer International Publishing",
address="Cham",
pages="119--127",
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. The code for this paper is freely available at https://github.com/baumgach/PHiSeg-code.",
isbn="978-3-030-32245-8"
}

@article{LogisticRegression,
 ISSN = {00359246},
 URL = {http://www.jstor.org/stable/2983890},
 abstract = {A sequence of 0's and 1's is observed and it is suspected that the chance that a particular trial is a 1 depends on the value of one or more independent variables. Tests and estimates for such situations are considered, dealing first with problems in which the independent variable is preassigned and then with independent variables that are functions of the sequence. There is a considerable amount of earlier work, which is reviewed.},
 author = {D. R. Cox},
 journal = {Journal of the Royal Statistical Society. Series B (Methodological)},
 number = {2},
 pages = {215--242},
 publisher = {[Royal Statistical Society, Wiley]},
 title = {The Regression Analysis of Binary Sequences},
 urldate = {2024-01-24},
 volume = {20},
 year = {1958}
}

@misc{kendall2016bayesian,
      title={{Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding}}, 
      author={Alex Kendall and Vijay Badrinarayanan and Roberto Cipolla},
      year={2016},
      eprint={1511.02680},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@misc{lakshminarayanan2017simple,
      title={Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles}, 
      author={Balaji Lakshminarayanan and Alexander Pritzel and Charles Blundell},
      year={2017},
      eprint={1612.01474},
      archivePrefix={arXiv},
      primaryClass={stat.ML}
}

@article{hwang2012glaucoma,
  title={{Glaucoma Diagnostic Ability of Quadrant and Clock-Hour Neuroretinal Rim Assessment Using Cirrus HD Optical Coherence Tomography}},
  author={Hwang, Young Hoon and Kim, Yong Yeon},
  journal={Investigative ophthalmology \& visual science},
  volume={53},
  number={4},
  pages={2226--2234},
  year={2012},
  publisher={The Association for Research in Vision and Ophthalmology}
}

@inproceedings{RIGA,
author = {Ahmed Almazroa and Sami Alodhayb and Essameldin  Osman and Eslam  Ramadan and Mohammed  Hummadi and Mohammed  Dlaim and Muhannad  Alkatee and Kaamran Raahemifar and Vasudevan  Lakshminarayanan},
title = {{Retinal fundus images for glaucoma analysis: the RIGA dataset}},
volume = {10579},
booktitle = {Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications},
editor = {Jianguo Zhang and Po-Hao Chen},
organization = {International Society for Optics and Photonics},
publisher = {SPIE},
pages = {105790B},
keywords = {Fundus images dataset, Glaucoma images dataset, RIGA dataset, Glaucoma images analysis},
year = {2018},
doi = {10.1117/12.2293584},
URL = {https://doi.org/10.1117/12.2293584}
}

@article{kumar2023chakṣu,
  title={Ch{\'a}kṣu: A glaucoma specific fundus image database},
  author={Kumar, JR Harish and Seelamantula, Chandra Sekhar and Gagan, JH and Kamath, Yogish S and Kuzhuppilly, Neetha IR and Vivekanand, U and Gupta, Preeti and Patil, Shilpa},
  journal={Scientific data},
  volume={10},
  number={1},
  pages={70},
  year={2023},
  publisher={Nature Publishing Group UK London}
}

@InProceedings{Ronneberger,
author={Ronneberger, Olaf
and Fischer, Philipp
and Brox, Thomas},
editor={Navab, Nassir
and Hornegger, Joachim
and Wells, William M.
and Frangi, Alejandro F.},
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},
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}
}



@misc{kingma2017adam,
      title={{Adam: A Method for Stochastic Optimization}}, 
      author={Diederik P. Kingma and Jimmy Ba},
      year={2017},
      eprint={1412.6980},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@article{Sevastopolsky_2017,
   title={{Optic disc and cup segmentation methods for glaucoma detection with modification of U-Net convolutional neural network}},
   volume={27},
   ISSN={1555-6212},
   url={http://dx.doi.org/10.1134/S1054661817030269},
   DOI={10.1134/s1054661817030269},
   number={3},
   journal={Pattern Recognition and Image Analysis},
   publisher={Pleiades Publishing Ltd},
   author={Sevastopolsky, A.},
   year={2017},
   month=jul, pages={618–624} }

@article{yu2019robust,
  title={{Robust optic disc and cup segmentation with deep learning for glaucoma detection}},
  author={Yu, Shuang and Xiao, Di and Frost, Shaun and Kanagasingam, Yogesan},
  journal={Computerized Medical Imaging and Graphics},
  volume={74},
  pages={61--71},
  year={2019},
  publisher={Elsevier}
}

@article{rasheed2023rimnet,
  title={RimNet: A Deep Neural Network Pipeline for Automated Identification of the Optic Disc Rim},
  author={Rasheed, Haroon Adam and Davis, Tyler and Morales, Esteban and Fei, Zhe and Grassi, Lourdes and De Gainza, Agustina and Nouri-Mahdavi, Kouros and Caprioli, Joseph},
  journal={Ophthalmology Science},
  volume={3},
  number={1},
  pages={100244},
  year={2023},
  publisher={Elsevier}
}

@article{tulsani2021automated,
  title={Automated segmentation of optic disc and optic cup for glaucoma assessment using improved UNET++ architecture},
  author={Tulsani, Akshat and Kumar, Preetham and Pathan, Sumaiya},
  journal={Biocybernetics and Biomedical Engineering},
  volume={41},
  number={2},
  pages={819--832},
  year={2021},
  publisher={Elsevier}
}

@inproceedings{edupuganti2018automatic,
  title={Automatic optic disk and cup segmentation of fundus images using deep learning},
  author={Edupuganti, Venkata Gopal and Chawla, Akshay and Kale, Amit},
  booktitle={2018 25th IEEE international conference on image processing (ICIP)},
  pages={2227--2231},
  year={2018},
  organization={IEEE}
}

@inproceedings{sevastopolsky2019stack,
  title={Stack-u-net: refinement network for improved optic disc and cup image segmentation},
  author={Sevastopolsky, Artem and Drapak, Stepan and Kiselev, Konstantin and Snyder, Blake M and Keenan, Jeremy D and Georgievskaya, Anastasia},
  booktitle={Medical Imaging 2019: Image Processing},
  volume={10949},
  pages={576--584},
  year={2019},
  organization={SPIE}

}@inproceedings{cheng2020probability,
  title={Probability distribution guided optic disc and cup segmentation from fundus images},
  author={Cheng, Pujin and Lyu, Junyan and Huang, Yijin and Tang, Xiaoying},
  booktitle={2020 42nd Annual International Conference of the IEEE Engineering in Medicine \& Biology Society (EMBC)},
  pages={1976--1979},
  year={2020},
  organization={IEEE}
}

@article{fauw2018clinically,
  title={Clinically applicable deep learning for diagnosis and referral in retinal disease},
  author={De Fauw, Jeffrey and Ledsam, Joseph R and Romera-Paredes, Bernardino and Nikolov, Stanislav and Tomasev, Nenad and Blackwell, Sam and Askham, Harry and Glorot, Xavier and O’Donoghue, Brendan and Visentin, Daniel and others},
  journal={Nature medicine},
  volume={24},
  number={9},
  pages={1342--1350},
  year={2018},
  publisher={Nature Publishing Group}
}


@Article{Sevastopolsky2017,
author={Sevastopolsky, A.},
title={Optic disc and cup segmentation methods for glaucoma detection with modification of U-Net convolutional neural network},
journal={Pattern Recognition and Image Analysis},
year={2017},
month={Jul},
day={01},
volume={27},
number={3},
pages={618-624},
abstract={Glaucoma is the second leading cause of blindness all over the world, with approximately 60 million cases reported worldwide in 2010. If undiagnosed in time, glaucoma causes irreversible damage to the optic nerve leading to blindness. The optic nerve head examination, which involves measurement of cup-todisc ratio, is considered one of the most valuable methods of structural diagnosis of the disease. Estimation of cup-to-disc ratio requires segmentation of optic disc and optic cup on eye fundus images and can be performed by modern computer vision algorithms. This work presents universal approach for automatic optic disc and cup segmentation, which is based on deep learning, namely, modification of U-Net convolutional neural network. Our experiments include comparison with the best known methods on publicly available databases DRIONS-DB, RIM-ONE v.3, DRISHTI-GS. For both optic disc and cup segmentation, our method achieves quality comparable to current state-of-the-art methods, outperforming them in terms of the prediction time.},
issn={1555-6212},
doi={10.1134/S1054661817030269},
url={https://doi.org/10.1134/S1054661817030269}
}

@Article{Singh2022,
author={Singh, Law Kumar
and {Pooja}
and Garg, Hitendra
and Khanna, Munish},
title={Deep learning system applicability for rapid glaucoma prediction from fundus images across various data sets},
journal={Evolving Systems},
year={2022},
month={Dec},
day={01},
volume={13},
number={6},
pages={807-836},
abstract={Glaucoma damages the optical nerve, which sends visual pictures to the brain, and results in irreversible vision loss. This chronic infection is the second leading cause of permanent blindness across the world and worsens the purpose of life if not cured at an early stage. Traditional ways of diagnosing glaucoma, however, rely on heavy equipment and highly trained personnel, making it impossible to assess huge populations of individuals. This results in high costs and lengthy wait times. As a result, new methods for diagnosing glaucoma that do not exacerbate these problems need to be investigated. Previously, to detect glaucoma through artificial intelligence, features were extracted manually, which not only consumes a lot of time but is also a tedious task to perform and there is a chance of intra-observer variability. Now, deep learning (DL) techniques can be used to extract features automatically, which was not possible in the traditional methods. In view of the multiple associated problems like limited labeled data, difficulty and cost incurred in building glaucoma fundus photographic datasets, and special hardware requirements, this study assessed the performance of a DL model (s) which are trained in detecting glaucoma from fundus pictures and methods. The objective is to present a versatile DL model which should generate auspicious performance across multiple datasets to meet real-life scenarios instead of generating specific dataset performance and, along with it, take care of these coupled problems. Diverse deep learning techniques are investigated in this empirical study to categorise the fundus images into two classes: normal and glaucomatous. On all these models, fine-tuning with transfer learning is also performed. Three different publicly available benchmark datasets (ACRIMA, ORIGA, and HRF) were used for training and validation. The models were tested not only on DRISHTI-GS (a public dataset) and a private dataset but also on twelve combinations of these five datasets. Extensive experiments are conducted to manifest the effectiveness of the proposed approach, and on the basis of Area under Curve values and computed accuracy values, it is concluded that Inception-ResNet-v2 and Xception models outperform other competitive models. The findings show the potential of this technology in the early identification of glaucoma. This automated diagnosis system has great potential to ultimately reduce the human efforts and precious time of ophthalmologists.},
issn={1868-6486},
doi={10.1007/s12530-022-09426-4},
url={https://doi.org/10.1007/s12530-022-09426-4}
}

@article{FAN2023100233,
title = {Detecting Glaucoma from Fundus Photographs Using Deep Learning without Convolutions: Transformer for Improved Generalization},
journal = {Ophthalmology Science},
volume = {3},
number = {1},
pages = {100233},
year = {2023},
issn = {2666-9145},
doi = {https://doi.org/10.1016/j.xops.2022.100233},
url = {https://www.sciencedirect.com/science/article/pii/S2666914522001221},
author = {Rui Fan and Kamran Alipour and Christopher Bowd and Mark Christopher and Nicole Brye and James A. Proudfoot and Michael H. Goldbaum and Akram Belghith and Christopher A. Girkin and Massimo A. Fazio and Jeffrey M. Liebmann and Robert N. Weinreb and Michael Pazzani and David Kriegman and Linda M. Zangwill},
keywords = {Deep learning, Fundus photographs, Glaucoma detection, Vision Transformers},
abstract = {Purpose
To compare the diagnostic accuracy and explainability of a Vision Transformer deep learning technique, Data-efficient image Transformer (DeiT), and ResNet-50, trained on fundus photographs from the Ocular Hypertension Treatment Study (OHTS) to detect primary open-angle glaucoma (POAG) and identify the salient areas of the photographs most important for each model’s decision-making process.
Design
Evaluation of a diagnostic technology.
Subjects, Participants, and Controls
Overall 66 715 photographs from 1636 OHTS participants and an additional 5 external datasets of 16 137 photographs of healthy and glaucoma eyes.
Methods
Data-efficient image Transformer models were trained to detect 5 ground-truth OHTS POAG classifications: OHTS end point committee POAG determinations because of disc changes (model 1), visual field (VF) changes (model 2), or either disc or VF changes (model 3) and Reading Center determinations based on disc (model 4) and VFs (model 5). The best-performing DeiT models were compared with ResNet-50 models on OHTS and 5 external datasets.
Main Outcome Measures
Diagnostic performance was compared using areas under the receiver operating characteristic curve (AUROC) and sensitivities at fixed specificities. The explainability of the DeiT and ResNet-50 models was compared by evaluating the attention maps derived directly from DeiT to 3 gradient-weighted class activation map strategies.
Results
Compared with our best-performing ResNet-50 models, the DeiT models demonstrated similar performance on the OHTS test sets for all 5 ground-truth POAG labels; AUROC ranged from 0.82 (model 5) to 0.91 (model 1). Data-efficient image Transformer AUROC was consistently higher than ResNet-50 on the 5 external datasets. For example, AUROC for the main OHTS end point (model 3) was between 0.08 and 0.20 higher in the DeiT than ResNet-50 models. The saliency maps from the DeiT highlight localized areas of the neuroretinal rim, suggesting important rim features for classification. The same maps in the ResNet-50 models show a more diffuse, generalized distribution around the optic disc.
Conclusions
Vision Transformers have the potential to improve generalizability and explainability in deep learning models, detecting eye disease and possibly other medical conditions that rely on imaging for clinical diagnosis and management.}
}

@inproceedings{eaton2018towards,
  title={Towards safe deep learning: accurately quantifying biomarker uncertainty in neural network predictions},
  author={Eaton-Rosen, Zach and Bragman, Felix and Bisdas, Sotirios and Ourselin, S{\'e}bastien and Cardoso, M Jorge},
  booktitle={Medical Image Computing and Computer Assisted Intervention--MICCAI 2018: 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part I},
  pages={691--699},
  year={2018},
  organization={Springer}
}

@article{mehta2021propagating,
  title={Propagating uncertainty across cascaded medical imaging tasks for improved deep learning inference},
  author={Mehta, Raghav and Christinck, Thomas and Nair, Tanya and Bussy, Aur{\'e}lie and Premasiri, Swapna and Costantino, Manuela and Chakravarthy, M Mallar and Arnold, Douglas L and Gal, Yarin and Arbel, Tal},
  journal={IEEE Transactions on Medical Imaging},
  volume={41},
  number={2},
  pages={360--373},
  year={2021},
  publisher={IEEE}
}

@inproceedings{mehta2019propagating,
  title={Propagating uncertainty across cascaded medical imaging tasks for improved deep learning inference},
  author={Mehta, Raghav and Christinck, Thomas and Nair, Tanya and Lemaitre, Paul and Arnold, Douglas and Arbel, Tal},
  booktitle={Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures: First International Workshop, UNSURE 2019, and 8th International Workshop, CLIP 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings 8},
  pages={23--32},
  year={2019},
  organization={Springer}
}

@inproceedings{fischer2023uncertainty,
  title={Uncertainty Estimation and Propagation in Accelerated MRI Reconstruction},
  author={Fischer, Paul and Thomas, K and Baumgartner, Christian F},
  booktitle={International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging},
  pages={84--94},
  year={2023},
  organization={Springer}
}

@inproceedings{feiner2023propagation,
  title={Propagation and Attribution of Uncertainty in Medical Imaging Pipelines},
  author={Feiner, Leonhard F and Menten, Martin J and Hammernik, Kerstin and Hager, Paul and Huang, Wenqi and Rueckert, Daniel and Braren, Rickmer F and Kaissis, Georgios},
  booktitle={International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging},
  pages={1--11},
  year={2023},
  organization={Springer}
}

@article{spaeth2002disc,
  title={The disc damage likelihood scale: reproducibility of a new method of estimating the amount of optic nerve damage caused by glaucoma.},
  author={Spaeth, George L and Henderer, Jeffrey and Liu, Connie and Kesen, Muge and Altangerel, Undraa and Bayer, Atilla and Katz, L Jay and Myers, Jonathan and Rhee, Douglas and Steinmann, William},
  journal={Transactions of the American Ophthalmological Society},
  volume={100},
  pages={181},
  year={2002},
  publisher={American Ophthalmological Society}
}

@article{kumar2019rim,
  title={Rim-to-disc ratio outperforms cup-to-disc ratio for glaucoma prescreening},
  author={Kumar, JR Harish and Seelamantula, Chandra Sekhar and Kamath, Yogish Subraya and Jampala, Rajani},
  journal={Scientific reports},
  volume={9},
  number={1},
  pages={7099},
  year={2019},
  publisher={Nature Publishing Group UK London}
}

@article{sohn2015learning,
  title={Learning structured output representation using deep conditional generative models},
  author={Sohn, Kihyuk and Lee, Honglak and Yan, Xinchen},
  journal={Advances in neural information processing systems},
  volume={28},
  year={2015}
}

@article{warfield2004simultaneous,
  title={Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation},
  author={Warfield, Simon K and Zou, Kelly H and Wells, William M},
  journal={IEEE transactions on medical imaging},
  volume={23},
  number={7},
  pages={903--921},
  year={2004},
  publisher={IEEE}
}

@article{de2023airogs,
  title={AIROGS: artificial intelligence for robust glaucoma screening challenge},
  author={De Vente, Coen and Vermeer, Koenraad A and Jaccard, Nicolas and Wang, He and Sun, Hongyi and Khader, Firas and Truhn, Daniel and Aimyshev, Temirgali and Zhanibekuly, Yerkebulan and Le, Tien-Dung and others},
  journal={IEEE transactions on medical imaging},
  year={2023},
  publisher={IEEE}
}

@article{mirzania2021applications,
  title={Applications of deep learning in detection of glaucoma: a systematic review},
  author={Mirzania, Delaram and Thompson, Atalie C and Muir, Kelly W},
  journal={European Journal of Ophthalmology},
  volume={31},
  number={4},
  pages={1618--1642},
  year={2021},
  publisher={SAGE Publications Sage UK: London, England}
}


%%% CDR classification
@misc{bi2019automated,
      title={Automated Segmentation of the Optic Disk and Cup using Dual-Stage Fully Convolutional Networks}, 
      author={Lei Bi and Yuyu Guo and Qian Wang and Dagan Feng and Michael Fulham and Jinman Kim},
      year={2019},
      eprint={1902.04713},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@article{bian2020optic,
  title={Optic disc and optic cup segmentation based on anatomy guided cascade network},
  author={Bian, Xuesheng and Luo, Xiongbiao and Wang, Cheng and Liu, Weiquan and Lin, Xiuhong},
  journal={Computer Methods and Programs in Biomedicine},
  volume={197},
  pages={105717},
  year={2020},
  publisher={Elsevier}
}

@article{al2018dense,
  title={Dense fully convolutional segmentation of the optic disc and cup in colour fundus for glaucoma diagnosis},
  author={Al-Bander, Baidaa and Williams, Bryan M and Al-Nuaimy, Waleed and Al-Taee, Majid A and Pratt, Harry and Zheng, Yalin},
  journal={Symmetry},
  volume={10},
  number={4},
  pages={87},
  year={2018},
  publisher={MDPI}
}

@article{jiang2019jointrcnn,
  title={JointRCNN: a region-based convolutional neural network for optic disc and cup segmentation},
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@article{zhang2023multiple,
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  journal={Applied Intelligence},
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  year={2023},
  publisher={Springer}
}

@article{neto2022evaluations,
  title={Evaluations of deep learning approaches for glaucoma screening using retinal images from mobile device},
  author={Neto, Alexandre and Camara, Jos{\'e} and Cunha, Ant{\'o}nio},
  journal={Sensors},
  volume={22},
  number={4},
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  publisher={MDPI}
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@article{abdar2023uncertaintyfusenet,
  title={UncertaintyFuseNet: robust uncertainty-aware hierarchical feature fusion model with ensemble Monte Carlo dropout for COVID-19 detection},
  author={Abdar, Moloud and Salari, Soorena and Qahremani, Sina and Lam, Hak-Keung and Karray, Fakhri and Hussain, Sadiq and Khosravi, Abbas and Acharya, U Rajendra and Makarenkov, Vladimir and Nahavandi, Saeid},
  journal={Information Fusion},
  volume={90},
  pages={364--381},
  year={2023},
  publisher={Elsevier}
}

@article{schmidt2023probabilistic,
  title={Probabilistic attention based on gaussian processes for deep multiple instance learning},
  author={Schmidt, Arne and Morales-Alvarez, Pablo and Molina, Rafael},
  journal={IEEE Transactions on Neural Networks and Learning Systems},
  year={2023},
  publisher={IEEE}
}

@article{abdar2021review,
  title={A review of uncertainty quantification in deep learning: Techniques, applications and challenges},
  author={Abdar, Moloud and Pourpanah, Farhad and Hussain, Sadiq and Rezazadegan, Dana and Liu, Li and Ghavamzadeh, Mohammad and Fieguth, Paul and Cao, Xiaochun and Khosravi, Abbas and Acharya, U Rajendra and others},
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}


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