Generative Adversarial Networks in Medical Image augmentation: A review.

Journal: Computers in biology and medicine
Published Date: Mar 5, 2022

Abstract

OBJECT: With the development of deep learning, the number of training samples for medical image-based diagnosis and treatment models is increasing. Generative Adversarial Networks (GANs) have attracted attention in medical image processing due to their excellent image generation capabilities and have been widely used in data augmentation. In this paper, a comprehensive and systematic review and analysis of medical image augmentation work are carried out, and its research status and development prospects are reviewed.

Authors

  • Yizhou Chen
    School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan 430070, China.
  • Xu-Hua Yang
    College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China. Electronic address: xhyang@zjut.edu.cn.
  • Zihan Wei
    College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China. Electronic address: 2112012201@zjut.edu.cn.
  • Ali Asghar Heidari
    College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China.
  • Nenggan Zheng
    Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, Zhejiang, China.
  • Zhicheng Li
    Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
  • Huiling Chen
    College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China.
  • Haigen Hu
  • Qianwei Zhou
    College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, PR China; Key Laboratory of Visual Media Intelligent Processing Technology of Zhejiang Province, Hangzhou 310023, PR China.
  • Qiu Guan

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