Classifying the molecular subtype of breast cancer using vision transformer and convolutional neural network features.

Journal: Breast cancer research and treatment
PMID: 39841349

Abstract

PURPOSE: Identification of the molecular subtypes in breast cancer allows to optimize treatment strategies, but usually requires invasive needle biopsy. Recently, non-invasive imaging has emerged as promising means to classify them. Magnetic resonance imaging is often used for this purpose because it is three-dimensional and highly informative. Instead, only a few reports have documented the use of mammograms. Given that mammography is the first choice for breast cancer screening, using it to classify molecular subtypes would allow for early intervention on a much wider scale. Here, we aimed to evaluate the effectiveness of combining global and local mammographic features by using Vision Transformer (ViT) and Convolutional Neural Network (CNN) to classify molecular subtypes in breast cancer.

Authors

  • Chiharu Kai
    Department of Radiological Technology, Faculty of Medical Technology, Niigata University of Health and Welfare, 1398 Shimamichou, Kita-Ku, Niigata, Japan.
  • Hideaki Tamori
    The Asahi Shimbun Company, Tokyo, Japan.
  • Tsunehiro Ohtsuka
    Ohtsuka Breastcare Clinic, Tokyo, Japan.
  • Miyako Nara
    Ohtsuka Breastcare Clinic, Tokyo, Japan.
  • Akifumi Yoshida
    Department of Radiological Technology, Faculty of Medical Technology, Niigata University of Health and Welfare, 1398 Shimamichou, Kita-Ku, Niigata, Japan.
  • Ikumi Sato
    Major in Health and Welfare, Graduate School of Niigata, University of Health and Welfare, Niigata, Japan.
  • Hitoshi Futamura
    KONICA MINOLTA, INC, Tokyo, Japan.
  • Naoki Kodama
    Department of Radiological Technology, Faculty of Medical Technology, Niigata University of Health and Welfare, 1398 Shimamichou, Kita-Ku, Niigata, Japan.
  • Satoshi Kasai
    Niigata University of Health and Welfare, Japan.

Keywords

External Resources

Popular Topics
Recent Journals