Primary Investigation of Deep Learning Models for Japanese "Group Classification" of Whole-Slide Images of Gastric Endoscopic Biopsy.

Journal: Computational and mathematical methods in medicine
Published Date: Sep 26, 2022

Abstract

BACKGROUND: Accurate pathological diagnosis of gastric endoscopic biopsy could greatly improve the opportunity of early diagnosis and treatment of gastric cancer. The Japanese "Group classification" of gastric biopsy corresponds well with the endoscopic diagnostic system and can guide clinical treatment. However, severe shortage of pathologists and their heavy workload limit the diagnostic accuracy. This study presents the first attempt to investigate the applicability and effectiveness of AI-aided system for automated Japanese "Group classification" of gastric endoscopic biopsy.

Authors

  • Xiaoya Fan
    Bio-, Electro- And Mechanical Systems, Université Libre de Bruxelles, Avenue F.D. Roosevelt 50 CP165/56, 1050, Brussels, Belgium. xiaoya.fan@ulb.ac.be.
  • Lihui Yu
    Dalian Municipal Central Hospital, Dalian, Liaoning, China.
  • Xin Qi
  • Xue Lin
    Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China.
  • Junjun Zhao
    Dalian Municipal Central Hospital, Dalian, Liaoning, China.
  • Dong Wang
    Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China.
  • Jing Zhang
    MOEMIL Laboratory, School of Optoelectronic Information, University of Electronic Science and Technology of China, Chengdu, China.

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