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

arXiv: https://arxiv.org/abs/2102.07978 · HTML: https://mlsanity.com/paper/2102.07978/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 35 references that are in the map.
- First posted on arXiv 16 Feb 2021.

## Builds on (most cited first, 4 of 4)

- [Caffe: Convolutional Architecture for Fast Feature Embedding](https://mlsanity.com/paper/1408.5093/) (2014)
- [Predicting Cardiovascular Risk Factors from Retinal Fundus Photographs using Deep Learning](https://mlsanity.com/paper/1708.09843/) (2017)
- [Application of Deep Learning in Fundus Image Processing for Ophthalmic Diagnosis -- A Review](https://mlsanity.com/paper/1812.07101/) (2018)
- [Multiclass segmentation as multitask learning for drusen segmentation in retinal optical coherence tomography](https://mlsanity.com/paper/1906.07679/) (2019)

## Built on by (most cited first, 30 of 34)

- [Deep Multimodal Learning with Missing Modality: A Survey](https://mlsanity.com/paper/2409.07825/) (2024)
- [M3Care: Learning with Missing Modalities in Multimodal Healthcare Data](https://mlsanity.com/paper/2210.17292/) (2022)
- [OLIVES Dataset: Ophthalmic Labels for Investigating Visual Eye Semantics](https://mlsanity.com/paper/2209.11195/) (2022)
- [Interpretable Bilingual Multimodal Large Language Model for Diverse Biomedical Tasks](https://mlsanity.com/paper/2410.18387/) (2024)
- [Delving into Semantic Scale Imbalance](https://mlsanity.com/paper/2212.14613/) (2022)
- [Geometric Prior Guided Feature Representation Learning for Long-Tailed Classification](https://mlsanity.com/paper/2401.11436/) (2024)
- [RetinalGPT: A Retinal Clinical Preference Conversational Assistant Powered by Large Vision-Language Models](https://mlsanity.com/paper/2503.03987/) (2025)
- [Medical Large Vision Language Models with Multi-Image Visual Ability](https://mlsanity.com/paper/2505.19031/) (2025)
- [Discriminative Kernel Convolution Network for Multi-Label Ophthalmic Disease Detection on Imbalanced Fundus Image Dataset](https://mlsanity.com/paper/2207.07918/) (2022)
- [Single-Stage Broad Multi-Instance Multi-Label Learning (BMIML) with Diverse Inter-Correlations and its application to medical image classification](https://mlsanity.com/paper/2209.02625/) (2022)
- [FunBench: Benchmarking Fundus Reading Skills of MLLMs](https://mlsanity.com/paper/2503.00901/) (2025)
- [Semi-weakly Supervised Contrastive Representation Learning for Retinal Fundus Images](https://mlsanity.com/paper/2108.02122/) (2021)
- [Federated Learning under Partially Class-Disjoint Data via Manifold Reshaping](https://mlsanity.com/paper/2405.18983/) (2024)
- [Cross- and Intra-image Prototypical Learning for Multi-label Disease Diagnosis and Interpretation](https://mlsanity.com/paper/2411.04607/) (2024)
- [FundusGAN: A Hierarchical Feature-Aware Generative Framework for High-Fidelity Fundus Image Generation](https://mlsanity.com/paper/2503.17831/) (2025)
- [Enhance Eye Disease Detection using Learnable Probabilistic Discrete Latents in Machine Learning Architectures](https://mlsanity.com/paper/2402.16865/) (2024)
- [Pathology-Aware Adaptive Watermarking for Text-Driven Medical Image Synthesis](https://mlsanity.com/paper/2503.08346/) (2025)
- [PSScreen: Partially Supervised Multiple Retinal Disease Screening](https://mlsanity.com/paper/2508.10549/) (2025)
- [Med-Banana: Learning Quality-Controlled Medical Image Editing from Success-and-Failure Trajectories](https://mlsanity.com/paper/2511.00801/) (2025)
- [A General Model for Retinal Segmentation and Quantification](https://mlsanity.com/paper/2602.07012/) (2026)
- [Managing Diabetic Retinopathy with Deep Learning: A Data Centric Overview](https://mlsanity.com/paper/2604.02448/) (2026)
- [SSP-RACL: Classification of Noisy Fundus Images with Self-Supervised Pretraining and Robust Adaptive Credal Loss](https://mlsanity.com/paper/2409.18147/) (2024)
- [Trends, Challenges, and Future Directions in Deep Learning for Glaucoma: A Systematic Review](https://mlsanity.com/paper/2411.05876/) (2024)
- [MediQ-GAN: Quantum-Inspired GAN for High Resolution Medical Image Generation](https://mlsanity.com/paper/2506.21015/) (2025)
- [Information Bottleneck-based Causal Attention for Multi-label Medical Image Recognition](https://mlsanity.com/paper/2508.08069/) (2025)
- [Detection of retinal diseases using an accelerated reused convolutional network](https://mlsanity.com/paper/2510.04232/) (2025)
- [PSScreen V2: Partially Supervised Multiple Retinal Disease Screening](https://mlsanity.com/paper/2510.22589/) (2025)
- [Climbing the label tree: Hierarchy-preserving contrastive learning for medical imaging](https://mlsanity.com/paper/2511.03771/) (2025)
- [EyeAgent: An Agentic AI System for Multimodal Clinical Decision Support in Ophthalmology](https://mlsanity.com/paper/2511.09394/) (2025)
- [MedForge: Interpretable Medical Deepfake Detection via Forgery-aware Reasoning](https://mlsanity.com/paper/2603.18577/) (2026)

## 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.