A Review of Deep Learning on Medical Image Analysis

Jian Wang, Hengde Zhu, Shui Hua Wang*, Yu Dong Zhang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

139 Citations (Scopus)

Abstract

Compared with common deep learning methods (e.g., convolutional neural networks), transfer learning is characterized by simplicity, efficiency and its low training cost, breaking the curse of small datasets. Medical image analysis plays an indispensable role in both scientific research and clinical diagnosis. Common medical image acquisition methods include Computer Tomography (CT), Magnetic Resonance Imaging (MRI), Ultrasound (US), X-Ray, etc. Although these medical imaging methods can be applied for non-invasive qualitative and quantitative analysis of patients—compared with image datasets in other computer vision fields such like faces—medical images, especially its labeling, is still scarce and insufficient. Therefore, more and more researchers adopted transfer learning for medical image processing. In this study, after reviewing one hundred representative papers from IEEE, Elsevier, Google Scholar, Web of Science and various sources published from 2000 to 2020, a comprehensive review is presented, including (i) structure of CNN, (ii) background knowledge of transfer learning, (iii) different types of strategies performing transfer learning, (iv) application of transfer learning in various sub-fields of medical image analysis, and (v) discussion on the future prospect of transfer learning in the field of medical image analysis. Through this review paper, beginners could receive an overall and systematic knowledge of transfer learning application in medical image analysis. And policymaker of related realm will benefit from the summary of the trend of transfer learning in medical imaging field and may be encouraged to make policy positive to the future development of transfer learning in the field of medical image analysis.

Original languageEnglish
Pages (from-to)351-380
Number of pages30
JournalMobile Networks and Applications
Volume26
Issue number1
DOIs
Publication statusPublished - Feb 2021
Externally publishedYes

Keywords

  • Artificial intelligence
  • CT
  • Convolutional neural networks
  • Deep learning
  • Feature extractor
  • Fine-tuning
  • MRI
  • Medical image analysis
  • Transfer learning

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