Deep-learning approach with convolutional neural network for classification of maximum intensity projections of dynamic contrast-enhanced breast magnetic resonance imaging.

Abstract

Authors

• Tomoyuki Fujioka
  Department of Diagnostic Radiology, Tokyo Medical and Dental University Hospital, 1-5-45, Yushima, Bunkyo-ku, Tokyo 113-8501, Japan.
• Yuka Yashima
  Department of Radiology, Dokkyo Medical University Saitama Medical Center, 2-1-50 Minami-koshigaya, Koshigaya 343-8555, Japan.
• Jun Oyama
  Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo, Japan.
• Mio Mori
  Department of Diagnostic Radiology, Tokyo Medical and Dental University Hospital, 1-5-45, Yushima, Bunkyo-ku, Tokyo 113-8501, Japan.
• Kazunori Kubota
  Department of Diagnostic Radiology, Tokyo Medical and Dental University Hospital, 1-5-45, Yushima, Bunkyo-ku, Tokyo 113-8501, Japan.
• Leona Katsuta
  Department of Diagnostic Radiology, Tokyo Medical and Dental University Hospital, 1-5-45, Yushima, Bunkyo-ku, Tokyo 113-8501, Japan.
• Koichiro Kimura
  Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo, Japan.
• Emi Yamaga
  Department of Diagnostic Radiology, Tokyo Medical and Dental University Hospital, 1-5-45, Yushima, Bunkyo-ku, Tokyo 113-8501, Japan.
• Goshi Oda
  Department of Surgery, Breast Surgery, Tokyo Medical and Dental University Hospital, 1-5-45, Yushima, Bunkyo-ku, Tokyo 113-8501, Japan.
• Tsuyoshi Nakagawa
  Department of Surgery, Breast Surgery, Tokyo Medical and Dental University Hospital, 1-5-45, Yushima, Bunkyo-ku, Tokyo 113-8501, Japan.
• Yoshio Kitazume
  Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo, Japan.
• Ukihide Tateishi
  Department of Diagnostic Radiology, Tokyo Medical and Dental University Hospital, 1-5-45, Yushima, Bunkyo-ku, Tokyo 113-8501, Japan.

Keywords

External Resources