mlsanityData to 7 Oct 2026

ADAM Challenge: Detecting Age-related Macular Degeneration from Fundus Images

arXiv 2202.07983 ↗First posted 16 Feb 2022eess.IV cs.CVcited by 25

Not on the map. Why?

Abstract (arXiv)

Age-related macular degeneration (AMD) is the leading cause of visual impairment among elderly in the world. Early detection of AMD is of great importance, as the vision loss caused by this disease is irreversible and permanent. Color fundus photography is the most cost-effective imaging modality to screen for retinal disorders. Cutting edge deep learning based algorithms have been recently developed for automatically detecting AMD from fundus images. However, there are still lack of a comprehensive annotated dataset and standard evaluation benchmarks. To deal with this issue, we set up the Automatic Detection challenge on Age-related Macular degeneration (ADAM), which was held as a satellite event of the ISBI 2020 conference. The ADAM challenge consisted of four tasks which cover the main aspects of detecting and characterizing AMD from fundus images, including detection of AMD, detection and segmentation of optic disc, localization of fovea, and detection and segmentation of lesions. As part of the challenge, we have released a comprehensive dataset of 1200 fundus images with AMD diagnostic labels, pixel-wise segmentation masks for both optic disc and AMD-related lesions (drusen, exudates, hemorrhages and scars, among others), as well as the coordinates corresponding to the location of the macular fovea. A uniform evaluation framework has been built to make a fair comparison of different models using this dataset. During the challenge, 610 results were submitted for online evaluation, with 11 teams finally participating in the onsite challenge. This paper introduces the challenge, the dataset and the evaluation methods, as well as summarizes the participating methods and analyzes their results for each task. In particular, we observed that the ensembling strategy and the incorporation of clinical domain knowledge were the key to improve the performance of the deep learning models.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). Builds on 24 of its 86 references that are in the map. First posted on arXiv 16 Feb 2022.

Builds on

24 of 86 references.

References · 24

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
  4. 2016Densely Connected Convolutional Networkscited by 6,691
  5. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
  6. 2016Feature Pyramid Networks for Object Detectioncited by 5,021
  7. 2017Squeeze-and-Excitation Networkscited by 4,763
  8. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,827
  9. 2016Aggregated Residual Transformations for Deep Neural Networkscited by 2,540
  10. 2017Rethinking Atrous Convolution for Semantic Image Segmentationcited by 2,154
  11. 2020Shortcut Learning in Deep Neural Networkscited by 1,928
  12. 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,518
12 more
  1. 2016Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learningcited by 1,509
  2. 2019Class-Balanced Loss Based on Effective Number of Samplescited by 1,239
  3. 2022Transformers in Medical Imaging: A Surveycited by 245
  4. 2019REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographscited by 226
  5. 2021Applications of Deep Learning in Fundus Images: A Reviewcited by 59
  6. 2022GAMMA Challenge:Glaucoma grAding from Multi-Modality imAgescited by 45
  7. 2019Patch-based Output Space Adversarial Learning for Joint Optic Disc and Cup Segmentationcited by 44
  8. 2018Disc-aware Ensemble Network for Glaucoma Screening from Fundus Imagecited by 39
  9. 2018Fully Convolutional Multi-scale Residual DenseNets for Cardiac Segmentation and Automated Cardiac Diagnosis using Ensemble of Classifierscited by 35
  10. 2018DeepSeeNet: A deep learning model for automated classification of patient-based age-related macular degeneration severity from color fundus photographscited by 21
  11. 2020AGE Challenge: Angle Closure Glaucoma Evaluation in Anterior Segment Optical Coherence Tomographycited by 15
  12. 2017An Ensemble Deep Learning Based Approach for Red Lesion Detection in Fundus Imagescited by 10

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 7; 2024: 3; 2025: 10; 2026: 2.

25 papers.

Sort:

By area:

  1. 2024OmniMedVQA: A New Large-Scale Comprehensive Evaluation Benchmark for Medical LVLMcited by 182
  2. 2022REFUGE2 Challenge: A Treasure Trove for Multi-Dimension Analysis and Evaluation in Glaucoma Screeningcited by 37
  3. 2024VisionUnite: A Vision-Language Foundation Model for Ophthalmology Enhanced with Clinical Knowledgecited by 19
  4. 2025DC-SAM: In-Context Segment Anything in Images and Videos via Dual Consistencycited by 15
  5. 2023Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directionscited by 14
  6. 2023PALM: Open Fundus Photograph Dataset with Pathologic Myopia Recognition and Anatomical Structure Annotationcited by 11
  7. 2025HOG-CNN: Integrating Histogram of Oriented Gradients with Convolutional Neural Networks for Retinal Image Classificationcited by 10
  8. 2022SSiT: Saliency-guided Self-supervised Image Transformer for Diabetic Retinopathy Gradingcited by 9
  9. 2023Data efficient deep learning for medical image analysis: A surveycited by 6
  10. 2025Medical Knowledge Intervention Prompt Tuning for Medical Image Classificationcited by 5
  11. 2024VascX Models: Model Ensembles for Retinal Vascular Analysis from Color Fundus Imagescited by 4
  12. 2025A Survey of Multimodal Ophthalmic Diagnostics: From Task-Specific Approaches to Foundational Modelscited by 3
13 more
  1. 2022Weakly-supervised detection of AMD-related lesions in color fundus images using explainable deep learningcited by 2
  2. 2023Learning Generalized Hybrid Proximity Representation for Image Recognitioncited by 2
  3. 2025Predicting Diabetic Macular Edema Treatment Responses Using OCT: Dataset and Methods of APTOS Competitioncited by 2
  4. 2025Boosting Medical Visual Understanding From Multi-Granular Language Learningcited by 2
  5. 2023Region and Spatial Aware Anomaly Detection for Fundus Imagescited by 1
  6. 2025PSScreen: Partially Supervised Multiple Retinal Disease Screeningcited by 1
  7. 2025Native Intelligence Emerges from Large-Scale Clinical Practice: A Retinal Foundation Model with Deployment Efficiencycited by 1
  8. 2026Towards Clinically Interpretable Ophthalmic VQA via Spatially-Grounded Lesion Evidencecited by 1
  9. 2023Learning Empirical Bregman Divergence for Uncertain Distance Representationcited by 0
  10. 2023The STOIC2021 COVID-19 AI challenge: applying reusable training methodologies to private datacited by 0
  11. 2025PSScreen V2: Partially Supervised Multiple Retinal Disease Screeningcited by 0
  12. 2025Surpassing state of the art on AMD area estimation from RGB fundus images through careful selection of U-Net architectures and loss functions for class imbalancecited by 0
  13. 2026EyeVQA: Benchmarking Ophthalmic Vision-Language Models from Recognition to Spatial Groundingcited by 0