Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (20): 42-52.DOI: 10.3778/j.issn.1002-8331.2106-0103

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Review of Application of Transfer Learning in Medical Image Analysis

LI Ying   

  1. Guangxi Colleges and Universities Key Laboratory of Scientific Computing and Intelligent Information Processing, Nanning Normal University, Nanning 530001, China
  • Online:2021-10-15 Published:2021-10-21



  1. 南宁师范大学 广西高校科学计算与智能信息处理重点实验室,南宁 530001


Transfer learning is a new learning paradigm in machine learning. It can overcome the defect that deep learning needs a large number of samples, and can solve the problem of inaccurate model caused by small data set in medical image analysis. Therefore, it has become a research hot spot in the field of medical image analysis after deep learning. Firstly, this paper briefly describes the transfer learning, and then according to the main transfer learning methods currently applied in medical image analysis, namely data-based transfer learning, model-based transfer learning, adversarial transfer learning and mixed transfer learning, sorts out and summarizes the important literature in the field of medical image analysis, and analyzes the mechanism, scope of application, application, advantages and disadvantages of each transfer learning method. Then, the paper summarizes, analyzes and compares these transfer learning methods. Finally, the paper points out the development trend of the research in this field according to the deficiency of the research status, and provides a reference for the further research of transfer learning in this field.

Key words: transfer learning, medical images, machine learning, learning paradigm



关键词: 迁移学习, 医学图像, 机器学习, 学习范式