  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.
