Computer-aided detection and prognosis of colorectal cancer on whole slide images using dual resolution deep learning.

Journal: Journal of cancer research and clinical oncology
PMID: 36331654

Abstract

PURPOSE: Rapid diagnosis and risk stratification can provide timely treatment for colorectal cancer (CRC) patients. Deep learning (DL) is not only used to identify tumor regions in histopathological images, but also applied to predict survival and achieve risk stratification. Whereas, most of methods dependent on regions of interest annotated by pathologist and ignore the global information in the image.

Authors

  • Yan Xu
    Department of Nephrology, Suzhou Ninth People's Hospital, Suzhou Ninth Hospital Affiliated to Soochow University, Suzhou, China.
  • Liwen Jiang
    Department of Pathology, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China.
  • Wenjing Chen
  • Shuting Huang
    School of Information Engineering, Guangdong University of Technology, Guangzhou, China.
  • Zhenyu Liu
    School of Electronic Information, Hangzhou Dianzi University, Hangzhou 310018, China.
  • Jiangyu Zhang
    Department of Pathology, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China zhenyuliu@gdut.edu.cn superchina2000@foxmail.com.

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