Factorised spatial representation learning: application in semi-supervised myocardial segmentation

Understand

The success and generalisation of deep learning algorithms heavily depend on learning good feature representations.

• In medical imaging this entails representing anatomical information, as well as properties related to the specific imaging setting.
• Anatomical information is required to perform further analysis, whereas imaging information is key to disentangle scanner variability and potential artefacts.
• The ability to factorise these would allow for training algorithms only on the relevant information according to the task.