Semi-supervised GAN-based Radiomics Model for Data Augmentation in Breast Ultrasound Mass Classification.

Journal: Computer methods and programs in biomedicine
Published Date: Feb 27, 2021

Abstract

BACKGROUND AND OBJECTIVE: The capability of deep learning radiomics (DLR) to extract high-level medical imaging features has promoted the use of computer-aided diagnosis of breast mass detected on ultrasound. Recently, generative adversarial network (GAN) has aided in tackling a general issue in DLR, i.e., obtaining a sufficient number of medical images. However, GAN methods require a pair of input and labeled images, which require an exhaustive human annotation process that is very time-consuming. The aim of this paper is to develop a radiomics model based on a semi-supervised GAN method to perform data augmentation in breast ultrasound images.

Authors

  • Ting Pang
    Center of Image and Signal Processing, Faculty of Computer Science and Infomation Technology, University of Malaya, Kuala Lumpur, 50603, Malaysia; Center of Network and Information, Xinxiang Medical University, Xinxiang, 453000, PR China.
  • Jeannie Hsiu Ding Wong
    Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur, 50603, Malaysia. Electronic address: jeannie.wong@ummc.edu.my.
  • Wei Lin Ng
    Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur, 50603, Malaysia.
  • Chee Seng Chan

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