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.

与普通的深度学习方法（例如卷积神经网络）相比，转移学习的特点是简单，高效且培训成本低，从而打破了小型数据集的魔咒。医学图像分析在科学研究和临床诊断中都扮演着不可或缺的角色。常见的医学图像采集方法包括计算机断层扫描（CT），磁共振成像（MRI），超声（US），X射线等。尽管这些医学成像方法可用于对患者进行无创定性和定量分析，但与之相比与其他计算机视觉领域（例如人脸）中的图像数据集相比，医学图像（尤其是其标签）仍然稀缺且不足。因此，越来越多的研究人员将转移学习用于医学图像处理。在这个研究中，在回顾了2000年至2020年发表的IEEE，Elsevier，Google Scholar，Web of Science和各种来源的一百篇代表性论文后，提出了全面的综述，包括（i）CNN的结构，（ii）转移学习的背景知识，（ iii）执行转移学习的不同类型的策略，（iv）转移学习在医学图像分析的各个子领域中的应用，以及（v）讨论转移学习在医学图像分析领域的未来前景。通过这篇综述文章，初学者可以全面了解和掌握转移学习在医学图像分析中的应用知识。