Learning disentangled representations in the imaging domain

Xiao Liu, Pedro Sanchez, Spyridon Thermos, Alison Q O’Neil, Sotirios A Tsaftaris

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Pages (from-to)102516
Number of pages1
JournalMedical Image Analysis
Early online date17 Jun 2022
Publication statusPublished - Aug 2022


  • Disentangled representation
  • Content-style
  • Applications
  • Tutorial
  • Medical Imaging
  • Computer Vision


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