  There has been an increasing focus in learning interpretable feature
representations, particularly in applications such as medical image analysis
that require explainability, whilst relying less on annotated data (since
annotations can be tedious and costly). Here we build on recent innovations in
style-content representations to learn anatomy, imaging characteristics
(appearance) and temporal correlations. By introducing a self-supervised
objective of predicting future cardiac phases we improve disentanglement. We
propose a temporal transformer architecture that given an image conditioned on
phase difference, it predicts a future frame. This forces the anatomical
decomposition to be consistent with the temporal cardiac contraction in cine
MRI and to have semantic meaning with less need for annotations. We demonstrate
that using this regularization, we achieve competitive results and improve
semi-supervised segmentation, especially when very few labelled data are
available. Specifically, we show Dice increase of up to 19\% and 7\% compared
to supervised and semi-supervised approaches respectively on the ACDC dataset.
Code is available at: https://github.com/gvalvano/sdtnet .
