# RetiGen: A Framework for Generalized Retinal Diagnosis Using Multi-View Fundus Images

arXiv: https://arxiv.org/abs/2403.15647 · HTML: https://mlsanity.com/paper/2403.15647/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 30 references that are in the map.
- First posted on arXiv 22 Mar 2024.

## Builds on (most cited first, 12 of 12)

- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Contrastive Test-Time Adaptation](https://mlsanity.com/paper/2204.10377/) (2022)
- [Fishr: Invariant Gradient Variances for Out-of-Distribution Generalization](https://mlsanity.com/paper/2109.02934/) (2021)
- [SwinMM: Masked Multi-view with Swin Transformers for 3D Medical Image Segmentation](https://mlsanity.com/paper/2307.12591/) (2023)
- [Deep learning in radiology: an overview of the concepts and a survey of the state of the art](https://mlsanity.com/paper/1802.08717/) (2018)
- [Learn to Segment Retinal Lesions and Beyond](https://mlsanity.com/paper/1912.11619/) (2019)
- [Deep Incomplete Multi-view Clustering with Cross-view Partial Sample and Prototype Alignment](https://mlsanity.com/paper/2303.15689/) (2023)
- [Towards Generalizable Diabetic Retinopathy Grading in Unseen Domains](https://mlsanity.com/paper/2307.04378/) (2023)
- [Improving Out-of-Distribution Generalization by Adversarial Training with Structured Priors](https://mlsanity.com/paper/2210.06807/) (2022)
- [GREEN: a Graph REsidual rE-ranking Network for Grading Diabetic Retinopathy](https://mlsanity.com/paper/2007.09968/) (2020)
- [Cross-Field Transformer for Diabetic Retinopathy Grading on Two-field Fundus Images](https://mlsanity.com/paper/2211.14552/) (2022)
- [Generalizing Across Domains in Diabetic Retinopathy via Variational Autoencoders](https://mlsanity.com/paper/2309.11301/) (2023)

## Abstract (arXiv)

This study introduces a novel framework for enhancing domain generalization in medical imaging, specifically focusing on utilizing unlabelled multi-view colour fundus photographs. Unlike traditional approaches that rely on single-view imaging data and face challenges in generalizing across diverse clinical settings, our method leverages the rich information in the unlabelled multi-view imaging data to improve model robustness and accuracy. By incorporating a class balancing method, a test-time adaptation technique and a multi-view optimization strategy, we address the critical issue of domain shift that often hampers the performance of machine learning models in real-world applications. Experiments comparing various state-of-the-art domain generalization and test-time optimization methodologies show that our approach consistently outperforms when combined with existing baseline and state-of-the-art methods. We also show our online method improves all existing techniques. Our framework demonstrates improvements in domain generalization capabilities and offers a practical solution for real-world deployment by facilitating online adaptation to new, unseen datasets. Our code is available at https://github.com/zgy600/RetiGen .