Dual resolution deep learning network with self-attention mechanism for classification and localisation of colorectal cancer in histopathological images.

Journal: Journal of clinical pathology
Published Date: Mar 10, 2022

Abstract

AIMS: Microscopic examination is a basic diagnostic technology for colorectal cancer (CRC), but it is very laborious. We developed a dual resolution deep learning network with self-attention mechanism (DRSANet) which combines context and details for CRC binary classification and localisation in whole slide images (WSIs), and as a computer-aided diagnosis (CAD) to improve the sensitivity and specificity of doctors' diagnosis.

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.
  • 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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