Deep-Learning-Based Characterization of Tumor-Infiltrating Lymphocytes in Breast Cancers From Histopathology Images and Multiomics Data.

Journal: JCO clinical cancer informatics
Published Date: May 1, 2020

Abstract

PURPOSE: Tumor-infiltrating lymphocytes (TILs) and their spatial characterizations on whole-slide images (WSIs) of histopathology sections have become crucial in diagnosis, prognosis, and treatment response prediction for different cancers. However, fully automatic assessment of TILs on WSIs currently remains a great challenge because of the heterogeneity and large size of WSIs. We present an automatic pipeline based on a cascade-training U-net to generate high-resolution TIL maps on WSIs.

Authors

  • Zixiao Lu
    School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China.
  • Siwen Xu
    Institute of Intelligent System and Bioinformatics, College of Automation, Harbin Engineering University, Harbin, Heilongjiang, People's Republic of China.
  • Wei Shao
  • Yi Wu
    School of International Communication and Arts, Hainan University, Haikou, China.
  • Jie Zhang
    College of Physical Education and Health, Linyi University, Linyi, Shandong, China.
  • Zhi Han
    School of Microelectronics, Southeast University, Wuxi 214135, China. 220153639@seu.edu.cn.
  • Qianjin Feng
    Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China. Electronic address: qianjinfeng08@gmail.com.
  • Kun Huang
    Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210, USA. Kun.Huang@osumc.edu.

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