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

Ze Chen; Gongyu Zhang; Jiayu Huo; Joan Nunez do Rio; Charalampos Komninos; Yang Liu; Rachel Sparks; Sebastien Ourselin; Christos Bergeles; Timothy Jackson

arXiv · 2403.15647

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

Ze Chen·Gongyu Zhang·Jiayu Huo·Joan Nunez do Rio·Charalampos Komninos·Yang Liu·Rachel Sparks·Sebastien Ourselin·Christos Bergeles·Timothy Jackson

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Abstract

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 .

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Ze Chen, Gongyu Zhang, Jiayu Huo, Joan Nunez do Rio, Charalampos Komninos, Yang Liu, Rachel Sparks, Sebastien Ourselin, Christos Bergeles, Timothy Jackson. 2024-03-22. RetiGen: A Framework for Generalized Retinal Diagnosis Using Multi-View Fundus Images. https://arxiv.org/abs/2403.15647

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