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

IntroductionDeep learning (DL) models predicting biomarker expression in images of hematoxylin and eosin (H&E)-stained tissues can improve access to multi-marker immunophenotyping, crucial for therapeutic monitoring, biomarker discovery, and personalized treatment development. Conventionally, th...

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Autors principals:	Abu Bakr Azam, Felicia Wee, Juha P. Väyrynen, Willa Wen-You Yim, Yue Zhen Xue, Bok Leong Chua, Jeffrey Chun Tatt Lim, Aditya Chidambaram Somasundaram, Daniel Shao Weng Tan, Angela Takano, Chun Yuen Chow, Li Yan Khor, Tony Kiat Hon Lim, Joe Yeong, Mai Chan Lau, Yiyu Cai
Format:	Artigo
Idioma:	Inglês
Publicat:	Frontiers Media S.A. 2024-06-01
Col·lecció:	Frontiers in Immunology
Matèries:	virtual staining CD3 Pix2Pix generative adversarial network (P2P-GAN) tumor-infiltrating lymphocytes (TILs) hematoxylin and eosin (H&E) ground truth cell label
Accés en línia:	https://www.frontiersin.org/articles/10.3389/fimmu.2024.1404640/full
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