A deep semi-supervised learning approach to the detection of glaucoma on out-of-distribution retinal fundus image datasets

Abstract Background Accurate detection of glaucoma plays a critical role in treating the disease and can be performed on limited labeled retinal fundus images and large-scale unlabeled ones leveraging a deep semi-supervised learning (SSL) technology. This study aims to investigate how glaucoma depic...

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Hlavní autoři:	Lei Wang, Xiaoyun Zhang, Zhongwen Li, Shuchen Yu, Yabo Wu, Shaodan Zhang, Gaoqiang Jiang, Bihan Tian, Chenyang Mei, Jiantao Pu, Yuanbo Liang, Quanyong Yi, Wencan Wu
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	BMC 2025-05-01
Edice:	BMC Ophthalmology
Témata:	Glaucoma detection Fundus images Semi-supervised learning Out-of-distribution Quantities and qualities of images
On-line přístup:	https://doi.org/10.1186/s12886-025-04153-1
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