Machine learning-based fundus image diagnosis technologies trigger worldwide interest owing to their benefits such as reducing medical resource power and providing objective evaluation results. However, current methods are commonly based on supervised methods, bringing in a heavy workload to biomedical staff and hence suffering in expanding effective databases. To address this issue, in this article, we established a label-free method, named "SSVT", which can automatically analyze un-labeled fundus images and generate high evaluation accuracy of 97.0% of four main eye diseases based on six public datasets and two datasets collected by Beijing Tongren Hospital. The promising results showcased the effectiveness of the proposed unsupervised learning method, and the strong application potential in biomedical resource shortage regions to improve global eye health.
基金：
National Natural Science Foundation [62171014]; EPSRC [EP/K03099X/1, EP/W024284/1]; UKRI Centre for Doctoral Training in AI for Healthcare [EP/S023283/1]; National Natural Science Foundation of China [82201244]; Natural Science Foundation of Beijing [M22019]; Beijing Hospitals Authority Innovation Studio of Young Staff Funding Support [202106]
语种：
外文
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第一作者机构：[1]Beihang Univ, Sch Instrumentat & Optoelect Engn, Beijing, Peoples R China
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推荐引用方式(GB/T 7714)：
Wang Jiaqi,Kang Mengtian,Liu Yong,et al.SSVT: Self-Supervised Vision Transformer For Eye Disease Diagnosis Based On Fundus Images[J].IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING, ISBI 2024.2024,doi:10.1109/ISBI56570.2024.10635531.
APA：
Wang, Jiaqi,Kang, Mengtian,Liu, Yong,Zhang, Chi,Liu, Ying...&Occhipinti, Luigi G..(2024).SSVT: Self-Supervised Vision Transformer For Eye Disease Diagnosis Based On Fundus Images.IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING, ISBI 2024,,
MLA：
Wang, Jiaqi,et al."SSVT: Self-Supervised Vision Transformer For Eye Disease Diagnosis Based On Fundus Images".IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING, ISBI 2024 .(2024)
