The wide application of deep learning techniques has strongly promoted the development of the medical image analysis field. However, most deep learning methods usually assume that the training and test sets are independently and identically distributed, and this assumption is problematic to guarantee when the models are deployed clinically. Hence, the dilemma of model performance degradation and poor scene generalization ability often occurs. Deep learning-based domain adaptation techniques are the mainstream methods for improving model migration ability. It aims to enable the model trained on one dataset to obtain better results on another dataset with no or only a small number of labels. Due to the difficulties in sample acquisition and labelling, unique image properties and modal differences in medical images bring many practical challenges to domain adaptive technology. This paper first introduces the definition and primary challenges of the domain adaptation and then classifies and summarizes related algorithms in recent years from a technical point of view, compare and analyze their advantages and disadvantages, and then introduce in detail the medical image datasets commonly used in domain adaptation and related algorithm results. Finally, this paper prospects the future research direction of domain adaptation for medical image analysis regarding development bottlenecks, technical means, and cross-cutting areas.

[2023-11-01] Accepted Paper /doc/2023.08.0379/v1