School of Engineering, The University of Edinburgh, Edinburgh EH9 3FG, UK.
School of Engineering, The University of Edinburgh, Edinburgh EH9 3FG, UK.
Med Image Anal. 2022 Aug;80:102516. doi: 10.1016/j.media.2022.102516. Epub 2022 Jun 17.
Disentangled representation learning has been proposed as an approach to learning general representations even in the absence of, or with limited, supervision. A good general representation can be fine-tuned for new target tasks using modest amounts of data, or used directly in unseen domains achieving remarkable performance in the corresponding task. This alleviation of the data and annotation requirements offers tantalising prospects for applications in computer vision and healthcare. In this tutorial paper, we motivate the need for disentangled representations, revisit key concepts, and describe practical building blocks and criteria for learning such representations. We survey applications in medical imaging emphasising choices made in exemplar key works, and then discuss links to computer vision applications. We conclude by presenting limitations, challenges, and opportunities.
解缠表示学习已被提出作为一种即使在缺乏监督或监督有限的情况下也能学习通用表示的方法。一个好的通用表示可以使用少量的数据进行微调，以适用于新的目标任务，或者直接在看不见的领域中使用，在相应的任务中实现显著的性能。这种对数据和注释要求的缓解为计算机视觉和医疗保健领域的应用提供了诱人的前景。在本教程中，我们将激发对解缠表示的需求，回顾关键概念，并描述学习这种表示的实用构建块和标准。我们将重点介绍医学成像中的应用，强调范例关键工作中的选择，然后讨论与计算机视觉应用的联系。最后，我们提出了限制、挑战和机会。
