mlsanityData to 7 Oct 2026

REFUGE2 Challenge: A Treasure Trove for Multi-Dimension Analysis and Evaluation in Glaucoma Screening

arXiv 2202.08994 ↗First posted 18 Feb 2022eess.IV cs.CVcited by 37

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Abstract (arXiv)

With the rapid development of artificial intelligence (AI) in medical image processing, deep learning in color fundus photography (CFP) analysis is also evolving. Although there are some open-source, labeled datasets of CFPs in the ophthalmology community, large-scale datasets for screening only have labels of disease categories, and datasets with annotations of fundus structures are usually small in size. In addition, labeling standards are not uniform across datasets, and there is no clear information on the acquisition device. Here we release a multi-annotation, multi-quality, and multi-device color fundus image dataset for glaucoma analysis on an original challenge -- Retinal Fundus Glaucoma Challenge 2nd Edition (REFUGE2). The REFUGE2 dataset contains 2000 color fundus images with annotations of glaucoma classification, optic disc/cup segmentation, as well as fovea localization. Meanwhile, the REFUGE2 challenge sets three sub-tasks of automatic glaucoma diagnosis and fundus structure analysis and provides an online evaluation framework. Based on the characteristics of multi-device and multi-quality data, some methods with strong generalizations are provided in the challenge to make the predictions more robust. This shows that REFUGE2 brings attention to the characteristics of real-world multi-domain data, bridging the gap between scientific research and clinical application.

Key facts. Cited by 37 papers in the map (data through 7 Oct 2026). Builds on 31 of its 76 references that are in the map. First posted on arXiv 18 Feb 2022.

Builds on

31 of 76 references.

References · 31

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19 more
  1. 2021Transformers in Vision: A Surveycited by 1,009
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  6. 2019REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographscited by 226
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  14. 2022ADAM Challenge: Detecting Age-related Macular Degeneration from Fundus Imagescited by 25
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Citing papers per year: 2022: 1; 2023: 2; 2024: 8; 2025: 16; 2026: 10.

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  1. 2025MedAgent-Pro: Towards Evidence-based Multi-modal Medical Diagnosis via Reasoning Agentic Workflowcited by 71
  2. 2023AIROGS: Artificial Intelligence for RObust Glaucoma Screening Challengecited by 35
  3. 2025LLM-based Agentic Reasoning Frameworks: A Survey from Methods to Scenarioscited by 25
  4. 2024A Survey of Medical Vision-and-Language Applications and Their Techniquescited by 11
  5. 2026Evolving Medical Imaging Agents via Experience-driven Self-skill Discoverycited by 10
  6. 2025Recent Advances in Medical Imaging Segmentation: A Surveycited by 8
  7. 2024MedUniSeg: 2D and 3D Medical Image Segmentation via a Prompt-driven Universal Modelcited by 7
  8. 2024SegICL: A Multimodal In-context Learning Framework for Enhanced Segmentation in Medical Imagingcited by 5
  9. 2024MedUHIP: Towards Human-In-the-Loop Medical Segmentationcited by 5
  10. 2025You Point, I Learn: Online Adaptation of Interactive Segmentation Models for Handling Distribution Shifts in Medical Imagingcited by 5
  11. 2025RetinaLogos: Fine-Grained Synthesis of High-Resolution Retinal Images Through Captionscited by 5
  12. 2023SynFundus-1M: A High-quality Million-scale Synthetic fundus images Dataset with Fifteen Types of Annotationcited by 4
25 more
  1. 2024VascX Models: Model Ensembles for Retinal Vascular Analysis from Color Fundus Imagescited by 4
  2. 2026Diffusion Model as a Generalist Segmentation Learnercited by 4
  3. 2022Calibrate the inter-observer segmentation uncertainty via diagnosis-first principlecited by 3
  4. 2024Towards Collective Intelligence: Uncertainty-aware SAM Adaptation for Ambiguous Medical Image Segmentationcited by 3
  5. 2025GONet: A Generalizable Deep Learning Model for Glaucoma Detectioncited by 3
  6. 2025Flow Stochastic Segmentation Networkscited by 3
  7. 2025SAM2LoRA: Composite Loss-Guided, Parameter-Efficient Finetuning of SAM2 for Retinal Fundus Segmentationcited by 3
  8. 2025Predicting Diabetic Macular Edema Treatment Responses Using OCT: Dataset and Methods of APTOS Competitioncited by 2
  9. 2025Semantics versus Identity: A Divide-and-Conquer Approach towards Adjustable Medical Image De-Identificationcited by 2
  10. 2024ETSCL: An Evidence Theory-Based Supervised Contrastive Learning Framework for Multi-modal Glaucoma Gradingcited by 1
  11. 2024SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentationcited by 1
  12. 2025Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentationcited by 1
  13. 2025PSScreen: Partially Supervised Multiple Retinal Disease Screeningcited by 1
  14. 2025Why Text Prevails: Vision May Undermine Multimodal Medical Decision Makingcited by 1
  15. 2026Weakly Supervised Segmentation as Semantic-Based Regularizationcited by 1
  16. 2025FunduSAM: A Specialized Deep Learning Model for Enhanced Optic Disc and Cup Segmentation in Fundus Imagescited by 0
  17. 2025PSScreen V2: Partially Supervised Multiple Retinal Disease Screeningcited by 0
  18. 2025A Dual-Mode ViT-Conditioned Diffusion Framework with an Adaptive Conditioning Bridge for Breast Cancer Segmentationcited by 0
  19. 2026MieDB-100k: A Comprehensive Dataset for Medical Image Editingcited by 0
  20. 2026Few-Shot Distribution-Aligned Flow Matching for Data Synthesis in Medical Image Segmentationcited by 0
  21. 2026MedFlowSeg: Flow Matching for Medical Image Segmentation with Frequency-Aware Attentioncited by 0
  22. 2026Scientific Domain Knowledge Improves Vision-Language Fundus Modelscited by 0
  23. 2026Learning Emergent Modular Representations in Multi-modality Medical Vision Foundation Modelscited by 0
  24. 2026MedRSI: Recursive Self-Improvement for Medical Agents via Clinically Aligned Self-Evolutioncited by 0
  25. 2026EyeVQA: Benchmarking Ophthalmic Vision-Language Models from Recognition to Spatial Groundingcited by 0