Convolutional neural network-based object detection model to identify gastrointestinal stromal tumors in endoscopic ultrasound images.

Journal: Journal of gastroenterology and hepatology
Published Date: Dec 1, 2021

Abstract

BACKGROUND AND AIM: We aimed to develop a convolutional neural network (CNN)-based object detection model for the discrimination of gastric subepithelial tumors, such as gastrointestinal stromal tumors (GISTs), and leiomyomas, in endoscopic ultrasound (EUS) images.

Authors

  • Chang Kyo Oh
    Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, South Korea.
  • Taewan Kim
    Department of Mechanical Engineering, Pohang University of Science and Technology (POSTECH), Pohang, South Korea.
  • Yu Kyung Cho
    Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, South Korea.
  • Dae Young Cheung
    Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea.
  • Bo-In Lee
    Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea.
  • Young-Seok Cho
    Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea.
  • Jin Il Kim
    Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, South Korea.
  • Myung-Gyu Choi
    Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea.
  • Han Hee Lee
    Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea.
  • Seungchul Lee
    Department of Mechanical Engineering, Pohang University of Science and Technology (POSTECH), 223, 5th Engineering Building 77 Cheongam-ro, Nam-Gu, Pohang, Gyeongbuk 37673, Republic of Korea. Electronic address: seunglee@postech.ac.kr.

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