An annotation-free whole-slide training approach to pathological classification of lung cancer types using deep learning

• Published in: Nature communications Vol. 12; no. 1; pp. 1193 - 13
• Main Authors: Chen, Chi-Long, Chen, Chi-Chung, Yu, Wei-Hsiang, Chen, Szu-Hua, Chang, Yu-Chan, Hsu, Tai-I, Hsiao, Michael, Yeh, Chao-Yuan, Chen, Cheng-Yu
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 19.02.2021; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/1305; 631/114/1564; 692/699/67/2321; Accelerators; Adenocarcinoma; Adenocarcinoma - pathology; Algorithms; Annotations; Carcinoma, Squamous Cell; Classification; Contouring; Deep Learning; Digital mapping; Humanities and Social Sciences; Humans; Image Processing, Computer-Assisted - methods; Localization; Lung cancer; Lung Neoplasms - classification; Lung Neoplasms - pathology; Machine learning; multidisciplinary; Neural networks; Neural Networks, Computer; Pathology; ROC Curve; Science; Science (multidisciplinary); Spatial discrimination; Spatial resolution; Squamous cell carcinoma; Training

Deep learning for digital pathology is hindered by the extremely high spatial resolution of whole-slide images (WSIs). Most studies have employed patch-based methods, which often require detailed annotation of image patches. This typically involves laborious free-hand contouring on WSIs. To alleviat...