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

Chi-Long Chen, Chi-Chung Chen, Wei-Hsiang Yu, Szu-Hua Chen, Yu-Chan Chang, Tai-I Hsu, Michael Hsiao, Chao-Yuan Yeh () and Cheng-Yu Chen ()
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Chi-Long Chen: Taipei Medical University
Chi-Chung Chen: aetherAI Co., Ltd.
Wei-Hsiang Yu: aetherAI Co., Ltd.
Szu-Hua Chen: aetherAI Co., Ltd.
Yu-Chan Chang: National Yang-Ming University
Tai-I Hsu: Academia Sinica
Michael Hsiao: Academia Sinica
Chao-Yuan Yeh: aetherAI Co., Ltd.
Cheng-Yu Chen: Taipei Medical University

Nature Communications, 2021, vol. 12, issue 1, 1-13

Abstract: Abstract 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.

Date: 2021
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DOI: 10.1038/s41467-021-21467-y

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