Learning from adversarial medical images for X-ray breast mass segmentation.

Journal: Computer methods and programs in biomedicine
Published Date:

Abstract

BACKGROUND AND OBJECTIVE: Simulation of diverse lesions in images is proposed and applied to overcome the scarcity of labeled data, which has hindered the application of deep learning in medical imaging. However, most of current studies focus on generating samples with class labels for classification and detection rather than segmentation, because generating images with precise masks remains a challenge. Therefore, we aim to generate realistic medical images with precise masks for improving lesion segmentation in mammagrams.

Authors

  • Tianyu Shen
    Institute of Automation, Chinese Academy of Sciences, Zhongguancun East Road 95, Beijing 100190, China; Qingdao Academy of Intelligent Industries, Zhilidao Road 1, Qingdao 266000, China; University of Chinese Academy of Sciences, Beijing 100049, China.
  • Chao Gou
    School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou 510275, China. Electronic address: gouchao.cas@gmail.com.
  • Fei-Yue Wang
  • Zilong He
    Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
  • Weiguo Chen
    Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China. Electronic address: chenweiguo1964@21cn.com.