Multistage deep learning for classification of Helicobacter pylori infection status using endoscopic images.

Journal: Journal of gastroenterology
PMID: 39815116

Abstract

BACKGROUND: The automated classification of Helicobacter pylori infection status is gaining attention, distinguishing among uninfected (no history of H. pylori infection), current infection, and post-eradication. However, this classification has relatively low performance, primarily due to the intricate nature of the task. This study aims to develop a new multistage deep learning method for automatically classifying H. pylori infection status.

Authors

  • Guang Li
    Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA.
  • Ren Togo
    Graduate School of Information Science and Technology, Hokkaido University, N-14, W-9, Kita-ku, Sapporo, Hokkaido, 060-0814, Japan.
  • Katsuhiro Mabe
    Department of Gastroenterology, National Hospital Organization Hakodate Hospital, 18-16, Kawahara-cho, Hakodate City, Hokkaido, 041-8512, Japan. katsumabe@me.com.
  • Shunpei Nishida
    Olympus Corporation, Tokyo, Japan.
  • Yoshihiro Tomoda
    Olympus Medical Systems Corporation, Tokyo, Japan.
  • Fumiyuki Shiratani
    Olympus Medical Systems Corporation, Tokyo, Japan.
  • Masashi Hirota
  • Takahiro Ogawa
    Faculty of Information Science and Technology, Hokkaido University, Sapporo, Japan.
  • Miki Haseyama
    Graduate School of Information Science and Technology, Hokkaido University, N-14, W-9, Kita-ku, Sapporo, Hokkaido, 060-0814, Japan.

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