A Fluorescence Imaging- and Deep Learning-Based Approach for Detecting Hepatitis B Virus Integration into Host Genomes.

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Bibliographic Details
Title: A Fluorescence Imaging- and Deep Learning-Based Approach for Detecting Hepatitis B Virus Integration into Host Genomes.
Authors: Yang TH; Department of Biomedical Engineering, Medical Device Innovation Center, National Cheng Kung University, Tainan, Taiwan.; Medical Device Innovation Center, National Cheng Kung University, Tainan, Taiwan., Chiu WT; Department of Biomedical Engineering, Medical Device Innovation Center, National Cheng Kung University, Tainan, Taiwan., Chu YH; Department of Medical Laboratory Science and Biotechnology, National Cheng Kung University, Tainan, Taiwan., Hong HT; Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan., Liu CW; Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan., Chang LY; Department of Biomedical Engineering, Medical Device Innovation Center, National Cheng Kung University, Tainan, Taiwan., Hung HC; Department of Medical Laboratory Science and Biotechnology, National Cheng Kung University, Tainan, Taiwan., Dai WC; Department of Biomedical Engineering, Medical Device Innovation Center, National Cheng Kung University, Tainan, Taiwan., Chen YL; Department of Pathology, National Cheng Kung University Hospital, Tainan, Taiwan., Lim SY; Department of Biotechnology and Bioindustry Sciences, National Cheng Kung University, Tainan, Taiwan., Chuang YC; Department of Biotechnology and Bioindustry Sciences, National Cheng Kung University, Tainan, Taiwan., Huang W; Department of Medical Laboratory Science and Biotechnology, National Cheng Kung University, Tainan, Taiwan. whuang@mail.ncku.edu.tw., Liu T; Department of Biotechnology and Bioindustry Sciences, National Cheng Kung University, Tainan, Taiwan. tsunglin@mail.ncku.edu.tw.
Source: Journal of imaging informatics in medicine [J Imaging Inform Med] 2026 May 18. Date of Electronic Publication: 2026 May 18.
Publication Type: Journal Article
Journal Info: Publisher: Springer Nature Country of Publication: Switzerland NLM ID: 9918663679206676 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2948-2933 (Electronic) Linking ISSN: 29482925 NLM ISO Abbreviation: J Imaging Inform Med Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2948-2933
DOI:10.1007/s10278-026-01983-3