RetiGen: A Framework for Generalized Retinal Diagnosis Using Multi-View Fundus Images
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909147857944576 |
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| author | Chen, Ze Zhang, Gongyu Huo, Jiayu Rio, Joan Nunez do Komninos, Charalampos Liu, Yang Sparks, Rachel Ourselin, Sebastien Bergeles, Christos Jackson, Timothy |
| author_facet | Chen, Ze Zhang, Gongyu Huo, Jiayu Rio, Joan Nunez do Komninos, Charalampos Liu, Yang Sparks, Rachel Ourselin, Sebastien Bergeles, Christos Jackson, Timothy |
| contents | 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 . |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_15647 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | RetiGen: A Framework for Generalized Retinal Diagnosis Using Multi-View Fundus Images Chen, Ze Zhang, Gongyu Huo, Jiayu Rio, Joan Nunez do Komninos, Charalampos Liu, Yang Sparks, Rachel Ourselin, Sebastien Bergeles, Christos Jackson, Timothy Computer Vision and Pattern Recognition 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 . |
| title | RetiGen: A Framework for Generalized Retinal Diagnosis Using Multi-View Fundus Images |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.15647 |