# Multi-Label Retinal Disease Classification using Transformers

arXiv: https://arxiv.org/abs/2207.02335 · HTML: https://mlsanity.com/paper/2207.02335/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 76 references that are in the map.
- First posted on arXiv 5 Jul 2022.

## Builds on (most cited first, 20 of 20)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [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)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Wide Residual Networks](https://mlsanity.com/paper/1605.07146/) (2016)
- [Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations](https://mlsanity.com/paper/1602.07332/) (2016)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Self-training with Noisy Student improves ImageNet classification](https://mlsanity.com/paper/1911.04252/) (2019)
- [EfficientNetV2: Smaller Models and Faster Training](https://mlsanity.com/paper/2104.00298/) (2021)
- [Focal Loss for Dense Object Detection](https://mlsanity.com/paper/1708.02002/) (2017)
- [Albumentations: fast and flexible image augmentations](https://mlsanity.com/paper/1809.06839/) (2018)
- [Asymmetric Loss For Multi-Label Classification](https://mlsanity.com/paper/2009.14119/) (2020)
- [Billion-scale semi-supervised learning for image classification](https://mlsanity.com/paper/1905.00546/) (2019)
- [General Multi-label Image Classification with Transformers](https://mlsanity.com/paper/2011.14027/) (2020)
- [PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions](https://mlsanity.com/paper/2204.12511/) (2022)
- [HPSLPred: An Ensemble Multi-label Classifier for Human Protein Subcellular Location Prediction with Imbalanced Source](https://mlsanity.com/paper/1704.05204/) (2017)

## Built on by (most cited first, 19 of 19)

- [Deep Learning for Multi-Label Learning: A Comprehensive Survey](https://mlsanity.com/paper/2401.16549/) (2024)
- [VisionUnite: A Vision-Language Foundation Model for Ophthalmology Enhanced with Clinical Knowledge](https://mlsanity.com/paper/2408.02865/) (2024)
- [CoBooM: Codebook Guided Bootstrapping for Medical Image Representation Learning](https://mlsanity.com/paper/2408.04262/) (2024)
- [Automatic Differential Diagnosis using Transformer-Based Multi-Label Sequence Classification](https://mlsanity.com/paper/2408.15827/) (2024)
- [Boosting Medical Visual Understanding From Multi-Granular Language Learning](https://mlsanity.com/paper/2511.15943/) (2025)
- [Project Imaging-X: A Survey of 1000+ Open-Access Medical Imaging Datasets for Foundation Model Development](https://mlsanity.com/paper/2603.27460/) (2026)
- [Adaptive Multiscale Retinal Diagnosis: A Hybrid Trio-Model Approach for Comprehensive Fundus Multi-Disease Detection Leveraging Transfer Learning and Siamese Networks](https://mlsanity.com/paper/2405.18449/) (2024)
- [Exploring Compositional Generalization of Multimodal LLMs for Medical Imaging](https://mlsanity.com/paper/2412.20070/) (2024)
- [Retinal Fundus Multi-Disease Image Classification using Hybrid CNN-Transformer-Ensemble Architectures](https://mlsanity.com/paper/2503.21465/) (2025)
- [GlaBoost: A Multimodal Structured Framework for Glaucoma Risk Stratification](https://mlsanity.com/paper/2508.03750/) (2025)
- [PSScreen: Partially Supervised Multiple Retinal Disease Screening](https://mlsanity.com/paper/2508.10549/) (2025)
- [PMP-Swin: Multi-Scale Patch Message Passing Swin Transformer for Retinal Disease Classification](https://mlsanity.com/paper/2311.11669/) (2023)
- [ECAMP: Entity-centered Context-aware Medical Vision Language Pre-training](https://mlsanity.com/paper/2312.13316/) (2023)
- [Explainable AI: Comparative Analysis of Normal and Dilated ResNet Models for Fundus Disease Classification](https://mlsanity.com/paper/2407.05440/) (2024)
- [Trends, Challenges, and Future Directions in Deep Learning for Glaucoma: A Systematic Review](https://mlsanity.com/paper/2411.05876/) (2024)
- [Information Bottleneck-based Causal Attention for Multi-label Medical Image Recognition](https://mlsanity.com/paper/2508.08069/) (2025)
- [PSScreen V2: Partially Supervised Multiple Retinal Disease Screening](https://mlsanity.com/paper/2510.22589/) (2025)
- [Quasi-multimodal-based pathophysiological feature learning for retinal disease diagnosis](https://mlsanity.com/paper/2602.03622/) (2026)
- [MAPLE: Multi-Path Adaptive Propagation with Level-Aware Embeddings for Hierarchical Multi-Label Image Classification](https://mlsanity.com/paper/2603.29784/) (2026)

## Abstract (arXiv)

Early detection of retinal diseases is one of the most important means of preventing partial or permanent blindness in patients. In this research, a novel multi-label classification system is proposed for the detection of multiple retinal diseases, using fundus images collected from a variety of sources. First, a new multi-label retinal disease dataset, the MuReD dataset, is constructed, using a number of publicly available datasets for fundus disease classification. Next, a sequence of post-processing steps is applied to ensure the quality of the image data and the range of diseases, present in the dataset. For the first time in fundus multi-label disease classification, a transformer-based model optimized through extensive experimentation is used for image analysis and decision making. Numerous experiments are performed to optimize the configuration of the proposed system. It is shown that the approach performs better than state-of-the-art works on the same task by 7.9% and 8.1% in terms of AUC score for disease detection and disease classification, respectively. The obtained results further support the potential applications of transformer-based architectures in the medical imaging field.