An explainable unsupervised learning approach for anomaly detection on corneal in vivo confocal microscopy images

BackgroundIn vivo confocal microscopy (IVCM) is a crucial imaging modality for assessing corneal diseases, yet distinguishing pathological features from normal variations remains challenging due to the complex multi-layered corneal structure. Existing anomaly detection methods often struggle to gene...

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Bibliographic Details
Main Authors:	Ningning Tang, Qi Chen, Yunyu Meng, Daizai Lei, Li Jiang, Yikun Qin, Xiaojia Huang, Fen Tang, Shanshan Huang, Qianqian Lan, Lijie Huang, Rushi Lan, Xipeng Pan, Huadeng Wang, Fan Xu, Wenjing He
Format:	Article
Language:	English
Published:	Frontiers Media S.A. 2025-06-01
Series:	Frontiers in Bioengineering and Biotechnology
Subjects:	corneal disease screening in vivo confocal microscopy (IVCM) unsupervised anomaly detection deep learning (DL) explainable artificial intelligence
Online Access:	https://www.frontiersin.org/articles/10.3389/fbioe.2025.1576513/full
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An explainable unsupervised learning approach for anomaly detection on corneal in vivo confocal microscopy images

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