mlsanityData to 13 Oct 2026

A Benchmark of Ocular Disease Intelligent Recognition: One Shot for Multi-disease Detection

arXiv 2102.07978 ↗First posted 16 Feb 2021cs.CV cs.AIcited by 34

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Abstract (arXiv)

In ophthalmology, early fundus screening is an economic and effective way to prevent blindness caused by ophthalmic diseases. Clinically, due to the lack of medical resources, manual diagnosis is time-consuming and may delay the condition. With the development of deep learning, some researches on ophthalmic diseases have achieved good results, however, most of them are just based on one disease. During fundus screening, ophthalmologists usually give diagnoses of multi-disease on binocular fundus image, so we release a dataset with 8 diseases to meet the real medical scene, which contains 10,000 fundus images from both eyes of 5,000 patients. We did some benchmark experiments on it through some state-of-the-art deep neural networks. We found simply increasing the scale of network cannot bring good results for multi-disease classification, and a well-structured feature fusion method combines characteristics of multi-disease is needed. Through this work, we hope to advance the research of related fields.

Key facts. Cited by 34 papers in the map (data through 13 Oct 2026). Builds on 4 of its 35 references that are in the map. First posted on arXiv 16 Feb 2021.

Builds on

4 of 35 references.

References · 4

  1. 2014Caffe: Convolutional Architecture for Fast Feature Embeddingcited by 287
  2. 2017Predicting Cardiovascular Risk Factors from Retinal Fundus Photographs using Deep Learningcited by 63
  3. 2018Application of Deep Learning in Fundus Image Processing for Ophthalmic Diagnosis -- A Reviewcited by 11
  4. 2019Multiclass segmentation as multitask learning for drusen segmentation in retinal optical coherence tomographycited by 2

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2021: 1; 2022: 5; 2023: 0; 2024: 8; 2025: 13; 2026: 7.

34 papers.

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  1. 2024Deep Multimodal Learning with Missing Modality: A Surveycited by 91
  2. 2022M3Care: Learning with Missing Modalities in Multimodal Healthcare Datacited by 71
  3. 2024Interpretable Bilingual Multimodal Large Language Model for Diverse Biomedical Taskscited by 24
  4. 2022OLIVES Dataset: Ophthalmic Labels for Investigating Visual Eye Semanticscited by 23
  5. 2022Delving into Semantic Scale Imbalancecited by 22
  6. 2024Geometric Prior Guided Feature Representation Learning for Long-Tailed Classificationcited by 10
  7. 2025RetinalGPT: A Retinal Clinical Preference Conversational Assistant Powered by Large Vision-Language Modelscited by 10
  8. 2025Medical Large Vision Language Models with Multi-Image Visual Abilitycited by 8
  9. 2022Discriminative Kernel Convolution Network for Multi-Label Ophthalmic Disease Detection on Imbalanced Fundus Image Datasetcited by 6
  10. 2022Single-Stage Broad Multi-Instance Multi-Label Learning (BMIML) with Diverse Inter-Correlations and its application to medical image classificationcited by 5
  11. 2025FunBench: Benchmarking Fundus Reading Skills of MLLMscited by 5
  12. 2021Semi-weakly Supervised Contrastive Representation Learning for Retinal Fundus Imagescited by 4
22 more
  1. 2024Federated Learning under Partially Class-Disjoint Data via Manifold Reshapingcited by 4
  2. 2024Cross- and Intra-image Prototypical Learning for Multi-label Disease Diagnosis and Interpretationcited by 4
  3. 2025FundusGAN: A Hierarchical Feature-Aware Generative Framework for High-Fidelity Fundus Image Generationcited by 2
  4. 2024Enhance Eye Disease Detection using Learnable Probabilistic Discrete Latents in Machine Learning Architecturescited by 1
  5. 2025Pathology-Aware Adaptive Watermarking for Text-Driven Medical Image Synthesiscited by 1
  6. 2025PSScreen: Partially Supervised Multiple Retinal Disease Screeningcited by 1
  7. 2025Med-Banana: Learning Quality-Controlled Medical Image Editing from Success-and-Failure Trajectoriescited by 1
  8. 2026A General Model for Retinal Segmentation and Quantificationcited by 1
  9. 2026Managing Diabetic Retinopathy with Deep Learning: A Data Centric Overviewcited by 1
  10. 2024SSP-RACL: Classification of Noisy Fundus Images with Self-Supervised Pretraining and Robust Adaptive Credal Losscited by 0
  11. 2024Trends, Challenges, and Future Directions in Deep Learning for Glaucoma: A Systematic Reviewcited by 0
  12. 2025MediQ-GAN: Quantum-Inspired GAN for High Resolution Medical Image Generationcited by 0
  13. 2025Information Bottleneck-based Causal Attention for Multi-label Medical Image Recognitioncited by 0
  14. 2025Detection of retinal diseases using an accelerated reused convolutional networkcited by 0
  15. 2025PSScreen V2: Partially Supervised Multiple Retinal Disease Screeningcited by 0
  16. 2025Climbing the label tree: Hierarchy-preserving contrastive learning for medical imagingcited by 0
  17. 2025EyeAgent: An Agentic AI System for Multimodal Clinical Decision Support in Ophthalmologycited by 0
  18. 2026MedForge: Interpretable Medical Deepfake Detection via Forgery-aware Reasoningcited by 0
  19. 2026Disentangling Co-Occurring Retinal Pathologies with Saliency-Guided Sparse Expert Routingcited by 0
  20. 2026Beyond Natural-Image Foundation Models: Benchmarking Satellite Pretraining for Ophthalmic Image Analysiscited by 0
  21. 2026EyeVQA: Benchmarking Ophthalmic Vision-Language Models from Recognition to Spatial Groundingcited by 0
  22. 2026Learning When to Recur: Token-Adaptive Recursion for Imbalanced Ophthalmic Domain Incremental Learningcited by 0