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Training immunophenotyping deep learning models with the same-section ground truth cell label derivation method improves virtual staining accuracy.

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Title: Training immunophenotyping deep learning models with the same-section ground truth cell label derivation method improves virtual staining accuracy.
Authors: Azam AB; School of Mechanical and Aerospace Engineering, College of Engineering, Nanyang Technological University, Singapore, Singapore., Wee F; Institute of Molecular and Cell Biology, Agency for Science, Technology and Research, Singapore, Singapore., Väyrynen JP; Translational Medicine Research Unit, Medical Research Center Oulu, Oulu University Hospital, and University of Oulu, Oulu, Finland., Yim WW; Institute of Molecular and Cell Biology, Agency for Science, Technology and Research, Singapore, Singapore., Xue YZ; Institute of Molecular and Cell Biology, Agency for Science, Technology and Research, Singapore, Singapore., Chua BL; School of Mechanical and Aerospace Engineering, College of Engineering, Nanyang Technological University, Singapore, Singapore., Lim JCT; Institute of Molecular and Cell Biology, Agency for Science, Technology and Research, Singapore, Singapore., Somasundaram AC; School of Electrical and Electronics Engineering, Nanyang Technological University, Singapore, Singapore., Tan DSW; Division of Medical Oncology, National Cancer Centre, Singapore, Singapore., Takano A; Department of Anatomical Pathology, Division of Pathology, Singapore General Hospital, Singapore, Singapore., Chow CY; Department of Anatomical Pathology, Division of Pathology, Singapore General Hospital, Singapore, Singapore., Khor LY; Department of Anatomical Pathology, Division of Pathology, Singapore General Hospital, Singapore, Singapore., Lim TKH; Department of Anatomical Pathology, Division of Pathology, Singapore General Hospital, Singapore, Singapore., Yeong J; Institute of Molecular and Cell Biology, Agency for Science, Technology and Research, Singapore, Singapore.; Department of Anatomical Pathology, Division of Pathology, Singapore General Hospital, Singapore, Singapore., Lau MC; Bioinformatics Institute, Agency for Science, Technology and Research, Matrix, Singapore, Singapore.; Singapore Immunology Network, Agency for Science, Technology and Research, Immunos, Singapore, Singapore., Cai Y; School of Mechanical and Aerospace Engineering, College of Engineering, Nanyang Technological University, Singapore, Singapore.
Source: Frontiers in immunology [Front Immunol] 2024 Jun 28; Vol. 15, pp. 1404640. Date of Electronic Publication: 2024 Jun 28 (Print Publication: 2024).
Publication Type: Journal Article
Journal Info: Publisher: Frontiers Research Foundation] Country of Publication: Switzerland NLM ID: 101560960 Publication Model: eCollection Cited Medium: Internet ISSN: 1664-3224 (Electronic) Linking ISSN: 16643224 NLM ISO Abbreviation: Front Immunol Subsets: MEDLINE
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        Value: 10.3389/fimmu.2024.1404640
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              Text: 2024 Jun 28
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