  Machine learning techniques used in computer-aided medical image analysis
usually suffer from the domain shift problem caused by different distributions
between source/reference data and target data. As a promising solution, domain
adaptation has attracted considerable attention in recent years. The aim of
this paper is to survey the recent advances of domain adaptation methods in
medical image analysis. We first present the motivation of introducing domain
adaptation techniques to tackle domain heterogeneity issues for medical image
analysis. Then we provide a review of recent domain adaptation models in
various medical image analysis tasks. We categorize the existing methods into
shallow and deep models, and each of them is further divided into supervised,
semi-supervised and unsupervised methods. We also provide a brief summary of
the benchmark medical image datasets that support current domain adaptation
research. This survey will enable researchers to gain a better understanding of
the current status, challenges.
