Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, India.
Comput Biol Med. 2024 Mar;170:107912. doi: 10.1016/j.compbiomed.2023.107912. Epub 2023 Dec 28.
Deep learning has demonstrated remarkable performance across various tasks in medical imaging. However, these approaches primarily focus on supervised learning, assuming that the training and testing data are drawn from the same distribution. Unfortunately, this assumption may not always hold true in practice. To address these issues, unsupervised domain adaptation (UDA) techniques have been developed to transfer knowledge from a labeled domain to a related but unlabeled domain. In recent years, significant advancements have been made in UDA, resulting in a wide range of methodologies, including feature alignment, image translation, self-supervision, and disentangled representation methods, among others. In this paper, we provide a comprehensive literature review of recent deep UDA approaches in medical imaging from a technical perspective. Specifically, we categorize current UDA research in medical imaging into six groups and further divide them into finer subcategories based on the different tasks they perform. We also discuss the respective datasets used in the studies to assess the divergence between the different domains. Finally, we discuss emerging areas and provide insights and discussions on future research directions to conclude this survey.
深度学习在医学影像的各种任务中表现出了显著的性能。然而，这些方法主要侧重于监督学习，假设训练和测试数据来自同一分布。不幸的是，这种假设在实践中并不总是成立。为了解决这些问题，已经开发了无监督领域自适应 (UDA) 技术，以将知识从有标签的领域转移到相关但无标签的领域。近年来，UDA 取得了重大进展，产生了多种方法，包括特征对齐、图像翻译、自监督和去纠缠表示方法等。在本文中，我们从技术角度对医学影像中的最新深度 UDA 方法进行了全面的文献综述。具体来说，我们将医学影像中的当前 UDA 研究分为六组，并根据它们执行的不同任务进一步细分为更细的子类别。我们还讨论了研究中使用的各个数据集，以评估不同领域之间的差异。最后，我们讨论了新兴领域，并对未来的研究方向提出了见解和讨论，以结束本调查。
