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
• DOI: 10.1038/s41467-021-21467-y
• ISSN: 2041-1723

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 alleviate the burden of such contouring and obtain benefits from scaling up training with numerous WSIs, we develop a method for training neural networks on entire WSIs using only slide-level diagnoses. Our method leverages the unified memory mechanism to overcome the memory constraint of compute accelerators. Experiments conducted on a data set of 9662 lung cancer WSIs reveal that the proposed method achieves areas under the receiver operating characteristic curve of 0.9594 and 0.9414 for adenocarcinoma and squamous cell carcinoma classification on the testing set, respectively. Furthermore, the method demonstrates higher classification performance than multiple-instance learning as well as strong localization results for small lesions through class activation mapping. Deep learning for digital pathology is hindered by the extremely high spatial resolution of whole slide images (WSIs), which requires researchers to adopt patch-based methods and laborious free-hand contouring. Here, the authors develop a whole-slide training method to classify types of lung cancers using slide-level diagnoses with deep learning.