  Longitudinal imaging is capable of capturing the static ana\-to\-mi\-cal
structures and the dynamic changes of the morphology resulting from aging or
disease progression. Self-supervised learning allows to learn new
representation from available large unlabelled data without any expert
knowledge. We propose a deep learning self-supervised approach to model disease
progression from longitudinal retinal optical coherence tomography (OCT). Our
self-supervised model takes benefit from a generic time-related task, by
learning to estimate the time interval between pairs of scans acquired from the
same patient. This task is (i) easy to implement, (ii) allows to use
irregularly sampled data, (iii) is tolerant to poor registration, and (iv) does
not rely on additional annotations. This novel method learns a representation
that focuses on progression specific information only, which can be transferred
to other types of longitudinal problems. We transfer the learnt representation
to a clinically highly relevant task of predicting the onset of an advanced
stage of age-related macular degeneration within a given time interval based on
a single OCT scan. The boost in prediction accuracy, in comparison to a network
learned from scratch or transferred from traditional tasks, demonstrates that
our pretrained self-supervised representation learns a clinically meaningful
information.
