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. 2021 Nov 10;11:763527. doi: 10.3389/fonc.2021.763527

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Copyright © 2021 Xie, Wang, Liang, Yang, Wu, Li, Sun, Bing, He, Tian and Shi

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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The flow chart for predicting cancer-related biomarkers based on digital pathological images. Firstly, H&E stained histology slides of patients were obtained and whole slide images (WSIs) was obtained after scanning. Secondly, tumor regions were annotated by pathologists or through CNN model. Then, the regions were segmented to patches and color-normalized. Thirdly, feature extraction and model training were carried out according to biomarker labels. Finally, biomarker prediction was implemented in test dataset.