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Learning disentangled representations in the imaging domain
Medical Image Analysis ( IF 10.9 ) Pub Date : 2022-06-17 , DOI: 10.1016/j.media.2022.102516
Xiao Liu 1 , Pedro Sanchez 1 , Spyridon Thermos 1 , Alison Q O'Neil 2 , Sotirios A Tsaftaris 3
Affiliation  

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.



中文翻译:

在成像领域学习解开的表示

即使在没有监督或监督有限的情况下,也有人提出了分离表示学习作为一种学习一般表示的方法。可以使用适度的数据量为新的目标任务微调一个好的通用表示,或者直接在看不见的领域中使用,从而在相应的任务中获得显着的性能。这种对数据和注释要求的缓解为计算机视觉和医疗保健中的应用提供了诱人的前景。在本教程论文中,我们激发了对解开表示的需求,重新审视了关键概念,并描述了学习此类表示的实际构建块和标准。我们调查医学成像中的应用,强调在示例性关键工作中做出的选择,然后讨论与计算机视觉应用的链接。

更新日期:2022-06-22
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