A hybrid multi-instance learning-based identification of gastric adenocarcinoma differentiation on whole-slide images.

Journal: Biomedical engineering online
Published Date:

Abstract

OBJECTIVE: To investigate the potential of a hybrid multi-instance learning model (TGMIL) combining Transformer and graph attention networks for classifying gastric adenocarcinoma differentiation on whole-slide images (WSIs) without manual annotation.

Authors

  • Mudan Zhang
    Department of Radiology, Guizhou Provincial People's Hospital, Guiyang, 550002 China.
  • Xinhuan Sun
    Department of Radiology, Guizhou Provincial Key Laboratory of Intelligent Medical Image Analysis and Precision Diagnosis, Guizhou Provincial People's Hospital, No. 83 Zhongshan East Road, Nan Ming District, Guiyang, 550002, Guizhou, China.
  • Wuchao Li
    Department of Radiology, Guizhou Provincial People's Hospital, Guiyang, China.
  • Yin Cao
    Department of Mathematics, Michigan State University, East Lansing , MI, 48824, USA.
  • Chen Liu
    Department of Radiology, Southwest Hospital, Third Military Medical University (Army Military Medical University), Chongqing, China.
  • Guilan Tu
    Laboratory department, Guizhou Provincial Center for Clinical Laboratory, Guiyang, 550002, Guizhou, China.
  • Jian Wang
    Veterinary Diagnostic Center, Shanghai Animal Disease Control Center, Shanghai, China.
  • Rongpin Wang
    Department of Radiology, Guizhou Provincial People's Hospital, Guiyang, 550002 China.