mlsanityData to 7 Oct 2026

AIROGS: Artificial Intelligence for RObust Glaucoma Screening Challenge

arXiv 2302.01738 ↗First posted 3 Feb 2023eess.IV cs.LGcited by 35

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Abstract (arXiv)

The early detection of glaucoma is essential in preventing visual impairment. Artificial intelligence (AI) can be used to analyze color fundus photographs (CFPs) in a cost-effective manner, making glaucoma screening more accessible. While AI models for glaucoma screening from CFPs have shown promising results in laboratory settings, their performance decreases significantly in real-world scenarios due to the presence of out-of-distribution and low-quality images. To address this issue, we propose the Artificial Intelligence for Robust Glaucoma Screening (AIROGS) challenge. This challenge includes a large dataset of around 113,000 images from about 60,000 patients and 500 different screening centers, and encourages the development of algorithms that are robust to ungradable and unexpected input data. We evaluated solutions from 14 teams in this paper, and found that the best teams performed similarly to a set of 20 expert ophthalmologists and optometrists. The highest-scoring team achieved an area under the receiver operating characteristic curve of 0.99 (95% CI: 0.98-0.99) for detecting ungradable images on-the-fly. Additionally, many of the algorithms showed robust performance when tested on three other publicly available datasets. These results demonstrate the feasibility of robust AI-enabled glaucoma screening.

Key facts. Cited by 35 papers in the map (data through 7 Oct 2026). Builds on 42 of its 98 references that are in the map. First posted on arXiv 3 Feb 2023.

Builds on

42 of 98 references.

References · 42

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  4. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  5. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  6. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,339
  7. 2016Densely Connected Convolutional Networkscited by 6,691
  8. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
  9. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
  10. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
  11. 2017Squeeze-and-Excitation Networkscited by 4,763
  12. 2020Training data-efficient image transformers & distillation through attentioncited by 4,560
30 more
  1. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  2. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,827
  3. 2022A ConvNet for the 2020scited by 3,545
  4. 2019RandAugment: Practical automated data augmentation with a reduced search spacecited by 2,261
  5. 2016Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learningcited by 1,509
  6. 2020Sharpness-Aware Minimization for Efficiently Improving Generalizationcited by 1,404
  7. 2019Self-training with Noisy Student improves ImageNet classificationcited by 1,304
  8. 2020Energy-based Out-of-distribution Detectioncited by 1,296
  9. 2018Unified Perceptual Parsing for Scene Understandingcited by 1,265
  10. 2021Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentationcited by 1,218
  11. 2021EfficientNetV2: Smaller Models and Faster Trainingcited by 1,127
  12. 2021UNETR: Transformers for 3D Medical Image Segmentationcited by 954
  13. 2017Focal Loss for Dense Object Detectioncited by 749
  14. 2018AutoAugment: Learning Augmentation Policies from Datacited by 728
  15. 2021ReAct: Out-of-distribution Detection With Rectified Activationscited by 486
  16. 2021Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysiscited by 418
  17. 2021Open-Set Recognition: a Good Closed-Set Classifier is All You Need?cited by 351
  18. 2019REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographscited by 226
  19. 2021TrivialAugment: Tuning-free Yet State-of-the-Art Data Augmentationcited by 225
  20. 2020Medical Image Segmentation Using Deep Learning: A Surveycited by 163
  21. 2021Revisiting ResNets: Improved Training and Scaling Strategiescited by 158
  22. 2021An Image is Worth 16x16 Words, What is a Video Worth?cited by 87
  23. 2022GAMMA Challenge:Glaucoma grAding from Multi-Modality imAgescited by 45
  24. 2019One-Class Convolutional Neural Networkcited by 37
  25. 2022REFUGE2 Challenge: A Treasure Trove for Multi-Dimension Analysis and Evaluation in Glaucoma Screeningcited by 37
  26. 2018Towards a glaucoma risk index based on simulated hemodynamics from fundus imagescited by 31
  27. 2021Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detectioncited by 23
  28. 2019Evaluation of an AI system for the automated detection of glaucoma from stereoscopic optic disc photographs: the European Optic Disc Assessment Studycited by 2
  29. 2022Computer Aided Diagnosis and Out-of-Distribution Detection in Glaucoma Screening Using Color Fundus Photographycited by 1
  30. 2022Deep Dirichlet uncertainty for unsupervised out-of-distribution detection of eye fundus photographs in glaucoma screeningcited by 1

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Citing papers per year: 2023: 4; 2024: 14; 2025: 14; 2026: 3.

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  1. 2024OmniMedVQA: A New Large-Scale Comprehensive Evaluation Benchmark for Medical LVLMcited by 182
  2. 2023On the Challenges and Perspectives of Foundation Models for Medical Image Analysiscited by 135
  3. 2025Baichuan-Omni-1.5 Technical Reportcited by 107
  4. 2023A Foundation Language-Image Model of the Retina (FLAIR): Encoding Expert Knowledge in Text Supervisioncited by 78
  5. 2024VisionUnite: A Vision-Language Foundation Model for Ophthalmology Enhanced with Clinical Knowledgecited by 19
  6. 2024Enhancing Diagnostic Accuracy in Rare and Common Fundus Diseases with a Knowledge-Rich Vision-Language Modelcited by 12
  7. 2024Training a high-performance retinal foundation model with half-the-data and 400 times less computecited by 9
  8. 2024Advancing Medical Image Segmentation with Mini-Net: A Lightweight Solution Tailored for Efficient Segmentation of Medical Imagescited by 9
  9. 2023Systematic comparison of semi-supervised and self-supervised learning for medical image classificationcited by 8
  10. 2025A Pragmatic Note on Evaluating Generative Models with Fr'echet Inception Distance for Retinal Image Synthesiscited by 8
  11. 2024Mask of truth: model sensitivity to unexpected regions of medical imagescited by 6
  12. 2025RetinaLogos: Fine-Grained Synthesis of High-Resolution Retinal Images Through Captionscited by 5
23 more
  1. 2023SynFundus-1M: A High-quality Million-scale Synthetic fundus images Dataset with Fifteen Types of Annotationcited by 4
  2. 2024Region Guided Attention Network for Retinal Vessel Segmentationcited by 4
  3. 2024Exploring the Transferability of a Foundation Model for Fundus Images: Application to Hypertensive Retinopathycited by 2
  4. 2024Evaluating Self-Supervised Learning in Medical Imaging: A Benchmark for Robustness, Generalizability, and Multi-Domain Impactcited by 2
  5. 2025Predicting Diabetic Macular Edema Treatment Responses Using OCT: Dataset and Methods of APTOS Competitioncited by 2
  6. 2025Boosting Medical Visual Understanding From Multi-Granular Language Learningcited by 2
  7. 2024DiffuPT: Class Imbalance Mitigation for Glaucoma Detection via Diffusion Based Generation and Model Pretrainingcited by 1
  8. 2024Exploring Compositional Generalization of Multimodal LLMs for Medical Imagingcited by 1
  9. 2025Enhancing Fundus Image-based Glaucoma Screening via Dynamic Global-Local Feature Integrationcited by 1
  10. 2025Clinician-Friendly Foundation Models for Ophthalmic Image Diagnostics without Fine-Tuning or Technical Barrierscited by 1
  11. 2025BioVessel-Net and RetinaMix: Unsupervised Retinal Vessel Segmentation from OCTA Imagescited by 1
  12. 2025Forging a Dynamic Memory: Retrieval-Guided Continual Learning for Generalist Medical Foundation Modelscited by 1
  13. 2025Native Intelligence Emerges from Large-Scale Clinical Practice: A Retinal Foundation Model with Deployment Efficiencycited by 1
  14. 2024Compute-Efficient Medical Image Classification with Softmax-Free Transformers and Sequence Normalizationcited by 0
  15. 2024Adenocarcinoma Segmentation Using Pre-trained Swin-UNet with Parallel Cross-Attention for Multi-Domain Imagingcited by 0
  16. 2024AI-Driven Approaches for Glaucoma Detection -- A Comprehensive Reviewcited by 0
  17. 2025Deep Learning for Ophthalmology: The State-of-the-Art and Future Trendscited by 0
  18. 2025Is Uncertainty Quantification a Viable Alternative to Learned Deferral?cited by 0
  19. 2025Automated Multi-label Classification of Eleven Retinal Diseases: A Benchmark of Modern Architectures and a Meta-Ensemble on a Large Synthetic Datasetcited by 0
  20. 2025Two-Stage Decoupling Framework for Variable-Length Glaucoma Prognosiscited by 0
  21. 2026M-IDoL: Information Decomposition for Modality-Specific and Diverse Representation Learning in Medical Foundation Modelcited by 0
  22. 2026Fundus Image-based Glaucoma Screening via Retinal Knowledge-Oriented Dynamic Multi-Level Feature Integrationcited by 0
  23. 2026EyeVQA: Benchmarking Ophthalmic Vision-Language Models from Recognition to Spatial Groundingcited by 0