Deep learning applied to two-dimensional color Doppler flow imaging ultrasound images significantly improves diagnostic performance in the classification of breast masses: a multicenter study.

Journal: Chinese medical journal
Published Date: Jan 7, 2021

Abstract

BACKGROUND: The current deep learning diagnosis of breast masses is mainly reflected by the diagnosis of benign and malignant lesions. In China, breast masses are divided into four categories according to the treatment method: inflammatory masses, adenosis, benign tumors, and malignant tumors. These categorizations are important for guiding clinical treatment. In this study, we aimed to develop a convolutional neural network (CNN) for classification of these four breast mass types using ultrasound (US) images.

Authors

  • Teng-Fei Yu
    Department of Ultrasound, Beijing Tian Tan Hospital, Capital Medical University, Beijing 100070, China.
  • Wen He
    Department of Ultrasound, Beijing Tian Tan Hospital, Capital Medical University, Beijing 100070, China.
  • Cong-Gui Gan
    Department of R&D, CHISON Medical Technologies Co., Ltd, Wuxi, Jiangsu 214028, China.
  • Ming-Chang Zhao
    Department of R&D, CHISON Medical Technologies Co., Ltd, Wuxi, Jiangsu 214028, China.
  • Qiang Zhu
  • Wei Zhang
    The First Affiliated Hospital of Nanchang University, Nanchang, China.
  • Hui Wang
    Department of Vascular Surgery, Xuanwu Hospital, Capital Medical University, Beijing, China.
  • Yu-Kun Luo
    Department of Ultrasound, Chinese PLA General Hospital, Beijing 100850, China.
  • Fang Nie
    Department of Ultrasound, Lanzhou University Second Hospital, Lanzhou, Gansu 730030, China.
  • Li-Jun Yuan
    Department of Ultrasound, Xi'an Tangdu Hospital of No. 4 Military Medical University, Xi'an, Shaanxi 710038, China.
  • Yong Wang
    State Key Laboratory of Chemical Biology and Drug Discovery, Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University Hunghom Kowloon Hong Kong P. R. China kwok-yin.wong@polyu.edu.hk.
  • Yan-Li Guo
    Department of Ultrasound, The Third Military Medical University Southwest Hospital, Chongqing 400038, China.
  • Jian-Jun Yuan
    Department of Ultrasound, Henan Provincial People's Hospital, Zhengzhou city, Henan 450003, China.
  • Li-Tao Ruan
    Department of Ultrasound, Xi'an Jiaotong University Medical College First Affiliated Hospital, Xi'an, Shaanxi 710061, China.
  • Yi-Cheng Wang
    Department of Ultrasound, Hebei Medical University First Affiliated Hospital, Zhangjiakou, Hebei 075061, China.
  • Rui-Fang Zhang
    Department of Ultrasound, Zhengzhou University First Affiliated Hospital, Zhengzhou, Henan 450052, China.
  • Hong-Xia Zhang
    Department of Ultrasound, Beijing Tian Tan Hospital, Capital Medical University, Beijing 100070, China.
  • Bin Ning
    Department of Ultrasound, Beijing Tian Tan Hospital, Capital Medical University, Beijing 100070, China.
  • Hai-Man Song
    Department of Ultrasound, Beijing Tian Tan Hospital, Capital Medical University, Beijing 100070, China.
  • Shuai Zheng
    Anhui Agricultural University Hefei 230036 PR China.
  • Yi Li
    Wuhan Zoncare Bio-Medical Electronics Co., Ltd, Wuhan, China.
  • Yang Guang
    Department of Ultrasound, Beijing Tian Tan Hospital, Capital Medical University, Beijing 100070, China.

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