Diagnosing uterine cervical cancer on a single T2-weighted image: Comparison between deep learning versus radiologists.

Abstract

Authors

• Aiko Urushibara
  Department of Radiology, Tsukuba Medical Center, 1-3-1 Amakubo, Tsukuba, Ibaraki, 305-0005, Japan. Electronic address: urushibara-kgw@umin.ac.jp.
• Tsukasa Saida
  Department of Radiology, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8575, Japan.
• Kensaku Mori
  Graduate School of Informatics, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8601, Japan.
• Toshitaka Ishiguro
  Department of Radiology, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8575, Japan.
• Masafumi Sakai
  Department of Radiology, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8575, Japan.
• Souta Masuoka
  Department of Radiology, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8575, Japan.
• Toyomi Satoh
  Department of Obstetrics and Gynecology, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8575, Japan.
• Tomohiko Masumoto
  Department of Radiology, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8575, Japan; Department of Radiology, Toranomon Hospital, 2-2-2 Toranomon, Minato-ku, Tokyo, 105-8470, Japan.

Keywords

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