A deep learning network based on multi-scale and attention for the diagnosis of chronic atrophic gastritis.

Journal: Zeitschrift fur Gastroenterologie
Published Date: Jun 13, 2022

Abstract

BACKGROUND AND STUDY AIM: Chronic atrophic gastritis plays an important role in the process of gastric cancer. Deep learning is gradually introduced in the medical field, and how to better apply a convolutional neural network (CNN) to the diagnosis of chronic atrophic gastritis remains a research hotspot. This study was designed to improve the performance of CNN on diagnosing chronic atrophic gastritis by constructing and evaluating a network structure based on the characteristics of gastroscopic images.

Authors

  • Yanwen Chong
    State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China.
  • Ningdi Xie
    Wuhan University, Wuhan City, China.
  • Xin Liu
    Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agricultural Sciences, Weifang, Shandong, China.
  • Meng Zhang
    College of Software, Beihang University, Beijing, China.
  • Fengxing Huang
    Wuhan University Zhongnan Hospital, Wuhan, China.
  • Jun Fang
    Core Laboratory, School of Medicine, Sichuan Provincial People's Hospital Affiliated to University of Electronic Science and Technology of China, Chengdu, P.R. China.
  • Fan Wang
    Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China.
  • Shaoming Pan
    State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China.
  • Haihang Nie
    Wuhan University Zhongnan Hospital, Wuhan, China.
  • Qiu Zhao
    Wuhan University, Wuhan City, China.

Keywords

External Resources

Popular Topics
Recent Journals