IEEE Trans Biomed Eng. 2022 Mar;69(3):1173-1185. doi: 10.1109/TBME.2021.3117407. Epub 2022 Feb 18.
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 and future directions of this energetic research field.
机器学习技术在计算机辅助医学图像分析中被广泛应用，但这些技术通常会受到源数据和目标数据分布不同导致的领域偏移问题的影响。作为一种有前途的解决方案，近年来，领域自适应已引起了相当多的关注。本文旨在综述医学图像分析中领域自适应方法的最新进展。我们首先介绍了将领域自适应技术引入医学图像分析以解决领域异质性问题的动机。然后，我们回顾了各种医学图像分析任务中最近的领域自适应模型。我们将现有的方法分为浅层和深层模型，并且每种方法都进一步分为有监督、半监督和无监督方法。我们还简要总结了支持当前领域自适应研究的基准医学图像数据集。本综述将使研究人员更好地了解这一充满活力的研究领域的现状、挑战和未来方向。
