Deep learning for image classification in dedicated breast positron emission tomography (dbPET).

Journal: Annals of nuclear medicine
Published Date: Jan 27, 2022

Abstract

OBJECTIVE: This study aimed to investigate and determine the best deep learning (DL) model to predict breast cancer (BC) with dedicated breast positron emission tomography (dbPET) images.

Authors

  • Yoko Satoh
  • Tomoki Imokawa
    Department of Diagnostic Radiology, Tokyo Medical and Dental University, Bunkyo Ku, Tokyo, Japan.
  • Tomoyuki Fujioka
    Department of Diagnostic Radiology, Tokyo Medical and Dental University Hospital, 1-5-45, Yushima, Bunkyo-ku, Tokyo 113-8501, Japan.
  • Mio Mori
    Department of Diagnostic Radiology, Tokyo Medical and Dental University Hospital, 1-5-45, Yushima, Bunkyo-ku, Tokyo 113-8501, Japan.
  • Emi Yamaga
    Department of Diagnostic Radiology, Tokyo Medical and Dental University Hospital, 1-5-45, Yushima, Bunkyo-ku, Tokyo 113-8501, Japan.
  • Kanae Takahashi
    Department of Medical Statistics, Osaka City University Graduate School of Medicine, Osaka, Japan.
  • Keiko Takahashi
    Department of Diagnostic Radiology, Tokyo Medical and Dental University, Bunkyo Ku, Tokyo, Japan.
  • Takahiro Kawase
    Department of Diagnostic Radiology, Tokyo Medical and Dental University, Bunkyo Ku, Tokyo, Japan.
  • Kazunori Kubota
    Department of Diagnostic Radiology, Tokyo Medical and Dental University Hospital, 1-5-45, Yushima, Bunkyo-ku, Tokyo 113-8501, Japan.
  • Ukihide Tateishi
    Department of Diagnostic Radiology, Tokyo Medical and Dental University Hospital, 1-5-45, Yushima, Bunkyo-ku, Tokyo 113-8501, Japan.
  • Hiroshi Onishi
    Department of Radiology, University of Yamanashi, Yamanashi, Japan.

Keywords

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