A knowledge-driven feature learning and integration method for breast cancer diagnosis on multi-sequence MRI.

Journal: Magnetic resonance imaging
Published Date: Mar 12, 2020

Abstract

BACKGROUND: The classification of benign versus malignant breast lesions on multi-sequence Magnetic Resonance Imaging (MRI) is a challenging task since breast lesions are heterogeneous and complex. Recently, deep learning methods have been used for breast lesion diagnosis with raw image input. However, without the guidance of domain knowledge, these data-driven methods cannot ensure that the features extracted from images are comprehensive for breast cancer diagnosis. Specifically, these features are difficult to relate to clinically relevant phenomena.

Authors

  • Hongwei Feng
    Department of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710127, China.
  • Jiaqi Cao
    Department of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710127, China.
  • Hongyu Wang
    School of Information Science and Technology, Northwest University, Xi'an, Shaanxi, China.
  • Yilin Xie
    Department of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710127, China.
  • Di Yang
    Clinical and Research Center of AIDS, Beijing Ditan Hospital, Capital Medical University, Beijing, China.
  • Jun Feng
    Linping Hospital of Integrated Traditional Chinese and Western, Medicine, Hangzhou, Zhejiang, China.
  • Baoying Chen
    Imaging Diagnosis and Treatment Center, Xi'an International Medical Center Hospital, Xi'an, 710100, P.R. China. Electronic address: chenby128@163.com.

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