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

arXiv: https://arxiv.org/abs/2202.08994 · HTML: https://mlsanity.com/paper/2202.08994/

## 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 (most cited first, 30 of 31)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [Transformers in Vision: A Survey](https://mlsanity.com/paper/2101.01169/) (2021)
- [Learning to Adapt Structured Output Space for Semantic Segmentation](https://mlsanity.com/paper/1802.10349/) (2018)
- [Res2Net: A New Multi-scale Backbone Architecture](https://mlsanity.com/paper/1904.01169/) (2019)
- [ResNeSt: Split-Attention Networks](https://mlsanity.com/paper/2004.08955/) (2020)
- [CSPNet: A New Backbone that can Enhance Learning Capability of CNN](https://mlsanity.com/paper/1911.11929/) (2019)
- [REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs](https://mlsanity.com/paper/1910.03667/) (2019)
- [CE-Net: Context Encoder Network for 2D Medical Image Segmentation](https://mlsanity.com/paper/1903.02740/) (2019)
- [Joint Optic Disc and Cup Segmentation Based on Multi-label Deep Network and Polar Transformation](https://mlsanity.com/paper/1801.00926/) (2018)
- [Source-Free Domain Adaptive Fundus Image Segmentation with Denoised Pseudo-Labeling](https://mlsanity.com/paper/2109.09735/) (2021)
- [Evaluation of Retinal Image Quality Assessment Networks in Different Color-spaces](https://mlsanity.com/paper/1907.05345/) (2019)
- [Boundary and Entropy-driven Adversarial Learning for Fundus Image Segmentation](https://mlsanity.com/paper/1906.11143/) (2019)
- [GAMMA Challenge:Glaucoma grAding from Multi-Modality imAges](https://mlsanity.com/paper/2202.06511/) (2022)
- [Patch-based Output Space Adversarial Learning for Joint Optic Disc and Cup Segmentation](https://mlsanity.com/paper/1902.07519/) (2019)
- [ADAM Challenge: Detecting Age-related Macular Degeneration from Fundus Images](https://mlsanity.com/paper/2202.07983/) (2022)
- [AGE Challenge: Angle Closure Glaucoma Evaluation in Anterior Segment Optical Coherence Tomography](https://mlsanity.com/paper/2005.02258/) (2020)
- [Few-Shot Domain Adaptation with Polymorphic Transformers](https://mlsanity.com/paper/2107.04805/) (2021)
- [Leveraging Undiagnosed Data for Glaucoma Classification with Teacher-Student Learning](https://mlsanity.com/paper/2007.11355/) (2020)
- [Two-stage framework for optic disc localization and glaucoma classification in retinal fundus images using deep learning](https://mlsanity.com/paper/2005.14284/) (2020)

## Built on by (most cited first, 30 of 37)

- [MedAgent-Pro: Towards Evidence-based Multi-modal Medical Diagnosis via Reasoning Agentic Workflow](https://mlsanity.com/paper/2503.18968/) (2025)
- [AIROGS: Artificial Intelligence for RObust Glaucoma Screening Challenge](https://mlsanity.com/paper/2302.01738/) (2023)
- [LLM-based Agentic Reasoning Frameworks: A Survey from Methods to Scenarios](https://mlsanity.com/paper/2508.17692/) (2025)
- [A Survey of Medical Vision-and-Language Applications and Their Techniques](https://mlsanity.com/paper/2411.12195/) (2024)
- [Evolving Medical Imaging Agents via Experience-driven Self-skill Discovery](https://mlsanity.com/paper/2603.05860/) (2026)
- [Recent Advances in Medical Imaging Segmentation: A Survey](https://mlsanity.com/paper/2505.09274/) (2025)
- [MedUniSeg: 2D and 3D Medical Image Segmentation via a Prompt-driven Universal Model](https://mlsanity.com/paper/2410.05905/) (2024)
- [SegICL: A Multimodal In-context Learning Framework for Enhanced Segmentation in Medical Imaging](https://mlsanity.com/paper/2403.16578/) (2024)
- [MedUHIP: Towards Human-In-the-Loop Medical Segmentation](https://mlsanity.com/paper/2408.01620/) (2024)
- [You Point, I Learn: Online Adaptation of Interactive Segmentation Models for Handling Distribution Shifts in Medical Imaging](https://mlsanity.com/paper/2503.06717/) (2025)
- [RetinaLogos: Fine-Grained Synthesis of High-Resolution Retinal Images Through Captions](https://mlsanity.com/paper/2505.12887/) (2025)
- [SynFundus-1M: A High-quality Million-scale Synthetic fundus images Dataset with Fifteen Types of Annotation](https://mlsanity.com/paper/2312.00377/) (2023)
- [VascX Models: Model Ensembles for Retinal Vascular Analysis from Color Fundus Images](https://mlsanity.com/paper/2409.16016/) (2024)
- [Diffusion Model as a Generalist Segmentation Learner](https://mlsanity.com/paper/2604.24575/) (2026)
- [Calibrate the inter-observer segmentation uncertainty via diagnosis-first principle](https://mlsanity.com/paper/2208.03016/) (2022)
- [Towards Collective Intelligence: Uncertainty-aware SAM Adaptation for Ambiguous Medical Image Segmentation](https://mlsanity.com/paper/2403.10931/) (2024)
- [GONet: A Generalizable Deep Learning Model for Glaucoma Detection](https://mlsanity.com/paper/2502.19514/) (2025)
- [Flow Stochastic Segmentation Networks](https://mlsanity.com/paper/2507.18838/) (2025)
- [SAM2LoRA: Composite Loss-Guided, Parameter-Efficient Finetuning of SAM2 for Retinal Fundus Segmentation](https://mlsanity.com/paper/2510.10288/) (2025)
- [Predicting Diabetic Macular Edema Treatment Responses Using OCT: Dataset and Methods of APTOS Competition](https://mlsanity.com/paper/2505.05768/) (2025)
- [Semantics versus Identity: A Divide-and-Conquer Approach towards Adjustable Medical Image De-Identification](https://mlsanity.com/paper/2507.21703/) (2025)
- [ETSCL: An Evidence Theory-Based Supervised Contrastive Learning Framework for Multi-modal Glaucoma Grading](https://mlsanity.com/paper/2407.14230/) (2024)
- [SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation](https://mlsanity.com/paper/2411.15513/) (2024)
- [Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation](https://mlsanity.com/paper/2502.03501/) (2025)
- [PSScreen: Partially Supervised Multiple Retinal Disease Screening](https://mlsanity.com/paper/2508.10549/) (2025)
- [Why Text Prevails: Vision May Undermine Multimodal Medical Decision Making](https://mlsanity.com/paper/2512.13747/) (2025)
- [Weakly Supervised Segmentation as Semantic-Based Regularization](https://mlsanity.com/paper/2605.13674/) (2026)
- [FunduSAM: A Specialized Deep Learning Model for Enhanced Optic Disc and Cup Segmentation in Fundus Images](https://mlsanity.com/paper/2502.06220/) (2025)
- [PSScreen V2: Partially Supervised Multiple Retinal Disease Screening](https://mlsanity.com/paper/2510.22589/) (2025)
- [A Dual-Mode ViT-Conditioned Diffusion Framework with an Adaptive Conditioning Bridge for Breast Cancer Segmentation](https://mlsanity.com/paper/2511.05989/) (2025)

## 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.