Breast cancer histopathology image classification through assembling multiple compact CNNs.

Journal: BMC medical informatics and decision making
Published Date: Oct 22, 2019

Abstract

BACKGROUND: Breast cancer causes hundreds of thousands of deaths each year worldwide. The early stage diagnosis and treatment can significantly reduce the mortality rate. However, the traditional manual diagnosis needs intense workload, and diagnostic errors are prone to happen with the prolonged work of pathologists. Automatic histopathology image recognition plays a key role in speeding up diagnosis and improving the quality of diagnosis.

Authors

  • Chuang Zhu
    The Center for Data Science, the Beijing Key Laboratory of Network System Architecture and Convergence, the School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Xitucheng Road, Beijing, China. czhu@bupt.edu.cn.
  • Fangzhou Song
    The Center for Data Science, the Beijing Key Laboratory of Network System Architecture and Convergence, the School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Xitucheng Road, Beijing, China.
  • Ying Wang
    Key Laboratory of Macromolecular Science of Shaanxi Province, School of Chemistry & Chemical Engineering, Shaanxi Normal University, Xi'an, Shaanxi 710062, China.
  • Huihui Dong
    The Center for Data Science, the Beijing Key Laboratory of Network System Architecture and Convergence, the School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Xitucheng Road, Beijing, China.
  • Yao Guo
  • Jun Liu
    Department of Radiology, Second Xiangya Hospital, Changsha, Hunan, China.

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