Two-stage CNNs for computerized BI-RADS categorization in breast ultrasound images.

Journal: Biomedical engineering online
Published Date: Jan 24, 2019

Abstract

BACKGROUND: Quantizing the Breast Imaging Reporting and Data System (BI-RADS) criteria into different categories with the single ultrasound modality has always been a challenge. To achieve this, we proposed a two-stage grading system to automatically evaluate breast tumors from ultrasound images into five categories based on convolutional neural networks (CNNs).

Authors

  • Yunzhi Huang
    Department of Biomedical Engineering, College of Materials Science and Engineering, Sichuan University, Chengdu, 610065, China.
  • Luyi Han
    College of Electrical Engineering and Information Technology, Sichuan University, Chengdu, 610065, China.
  • Haoran Dou
    National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University, Shenzhen, 518060, China.
  • Honghao Luo
    Department of Ultrasound, West China Hospital of Sichuan University, Chengdu, 610041, China.
  • Zhen Yuan
    Bioimaging Core, Faculty of Health Sciences, University of Macau, Macau SAR, China.
  • Qi Liu
    National Institute of Traditional Chinese Medicine Constitution and Preventive Medicine, Beijing University of Chinese Medicine, Beijing, China.
  • Jiang Zhang
    College of Electrical Engineering and Information Technology, Sichuan University, Chengdu, 610065, China.
  • Guangfu Yin
    Department of Biomedical Engineering, College of Materials Science and Engineering, Sichuan University, Chengdu, 610065, China. yingf@scu.edu.cn.

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