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

• URL: http://arxiv.org/abs/2403.15647v1
• Date: Fri, 22 Mar 2024 23:08:31 GMT
• Title: RetiGen: A Framework for Generalized Retinal Diagnosis Using Multi-View Fundus Images
• Authors: Ze Chen, Gongyu Zhang, Jiayu Huo, Joan Nunez do Rio, Charalampos Komninos, Yang Liu, Rachel Sparks, Sebastien Ourselin, Christos Bergeles, Timothy Jackson,
• Abstract summary: This study introduces a novel framework for enhancing domain generalization in medical imaging. Our method leverages the rich information in the unlabelled multi-view imaging data to improve model robustness and accuracy. Our framework demonstrates improvements in domain generalization capabilities and offers a practical solution for real-world deployment.
• Score: 7.4517363785335196
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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