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Integrating prior knowledge with deep learning for optimized quality control in corneal images: A multicenter study.

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Title: Integrating prior knowledge with deep learning for optimized quality control in corneal images: A multicenter study.
Authors: Li FF; National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, PR China., Li GX; Beijing Normal University, School of Artificial Intelligence, Beijing, PR China., Yu XX; National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, PR China., Zhang ZH; National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, PR China., Fu YN; National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, PR China., Wu SQ; National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, PR China., Wang Y; The First People's Hospital of Aksu District in Xinjiang, Aksu, PR China., Xiao C; The First People's Hospital of Aksu District in Xinjiang, Aksu, PR China., Ye YF; National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, PR China., Hu M; Aier Guangming Eye Hospital, Ningbo, PR China. Electronic address: tuzibuhao@163.com., Dai Q; National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, PR China. Electronic address: dq@mail.eye.ac.cn.
Source: Computer methods and programs in biomedicine [Comput Methods Programs Biomed] 2025 Jul; Vol. 267, pp. 108814. Date of Electronic Publication: 2025 Apr 28.
Publication Type: Journal Article; Multicenter Study
Journal Info: Publisher: Elsevier Scientific Publishers Country of Publication: Ireland NLM ID: 8506513 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1872-7565 (Electronic) Linking ISSN: 01692607 NLM ISO Abbreviation: Comput Methods Programs Biomed Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1872-7565
DOI:10.1016/j.cmpb.2025.108814