Performance of a deep learning-based identification system for esophageal cancer from CT images.

Journal: Esophagus : official journal of the Japan Esophageal Society
Published Date: Feb 26, 2021

Abstract

BACKGROUND: Because cancers of hollow organs such as the esophagus are hard to detect even by the expert physician, it is important to establish diagnostic systems to support physicians and increase the accuracy of diagnosis. In recent years, deep learning-based artificial intelligence (AI) technology has been employed for medical image recognition. However, no optimal CT diagnostic system employing deep learning technology has been attempted and established for esophageal cancer so far.

Authors

  • Masashi Takeuchi
    Department of Surgery Keio University School of Medicine Tokyo Japan.
  • Takumi Seto
    Department of Biosciences and Informatics, Keio University, 3-13-1 Hiyoshi, Kohoku-ku, Yokohama, 223-8522, Japan.
  • Masahiro Hashimoto
    Department of Radiology, Keio University School of Medicine, Tokyo, Japan. m.hashimoto@rad.med.keio.ac.jp.
  • Nao Ichihara
    Department of Health Policy Management, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan.
  • Yosuke Morimoto
    Department of Surgery, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan.
  • Hirofumi Kawakubo
    Department of Surgery, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan.
  • Tatsuya Suzuki
    Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan.
  • Masahiro Jinzaki
    Department of Radiology, Keio University School of Medicine, Tokyo, Japan.
  • Yuko Kitagawa
    Department of Surgery Keio University School of Medicine Tokyo Japan.
  • Hiroaki Miyata
    Department of Health Policy Management, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan.
  • Yasubumi Sakakibara
    Department of Biosciences and Informatics, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan yasu@bio.keio.ac.jp.

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