Identifying pathological slices of gastric cancer via deep learning.

Journal: Journal of the Formosan Medical Association = Taiwan yi zhi
Published Date: Jun 3, 2022

Abstract

BACKGROUND: The accuracy of histopathology diagnosis largely depends on the pathologist's experience. It usually takes over 10 years to cultivate a senior pathologist, and small numbers of them lead to a high workload for those available. Meanwhile, inconsistent diagnostic results may arise among different pathologists, especially in complex cases, because diagnosis based on morphology is subjective. Computerized analysis based on deep learning has shown potential benefits as a diagnostic strategy.

Authors

  • Chun-Liang Tung
    Department of Pathology, Ditmanson Medical Foundation Chiayi Christian Hospital, Chiayi, Taiwan; Department of Health and Nutrition Biotechnology, Asia University, Taichung, Taiwan.
  • Han-Cheng Chang
    Department of Computer Science & Information Engineering, National Chung Cheng University, Chiayi, Taiwan; Information Technology Department, Ditmanson Medical Foundation Chiayi Christian Hospital, Chiayi, Taiwan.
  • Bo-Zhi Yang
    Department of Computer Science & Information Engineering, National Chung Cheng University, Chiayi, Taiwan; Information Technology Department, Ditmanson Medical Foundation Chiayi Christian Hospital, Chiayi, Taiwan.
  • Keng-Jen Hou
    Information Technology Department, Ditmanson Medical Foundation Chiayi Christian Hospital, Chiayi, Taiwan.
  • Hung-Hsu Tsai
    Department of Applied Mathematics, Institute of Data Science and Information Computing National Chung Hsing University.
  • Cheng-Yu Tsai
    Graduate Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan.
  • Pao-Ta Yu
    Department of Computer Science & Information Engineering, National Chung Cheng University, Chiayi, Taiwan. Electronic address: csipty@cs.ccu.edu.tw.

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