Open AccessHigh‐Throughput, Label‐Free and Slide‐Free Histological Imaging by Computational Microscopy and Unsupervised Learning (Adv. Sci. 2/2022)
Author(s) -
Zhang Yan,
Kang Lei,
Wong Ivy H. M.,
Dai Weixing,
Li Xiufeng,
Chan Ronald C. K.,
Hsin Michael K. Y.,
Wong Terence T. W.
Publication year - 2022
Publication title -
advanced science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.388
H-Index - 100
ISSN - 2198-3844
DOI - 10.1002/advs.202270012
Subject(s) - microscopy , histopathology , throughput , high resolution , deep learning , digital pathology , biomedical engineering , computer science , artificial intelligence , materials science , pathology , medicine , telecommunications , remote sensing , wireless , geology
Label‐Free and Slide‐Free Histological Imaging Method In article number 2102358 by Terence T. W. Wong and co‐workers, a high‐throughput, label‐free, and slide‐free histological imaging method (termed CHAMP) is proposed. Assisted by computational microscopy and unsupervised learning, virtually stained Deep‐CHAMP images show high‐resolution histopathological cellular features of thick and unprocessed tissues with large surface irregularity. Rich cellular contents in cancerous human lung tissues are revealed by Deep‐CHAMP with high accuracy, holding great promise to revolutionize the standard‐of‐care histopathology.