  A significant proportion of patients scanned in a clinical setting have
follow-up scans. We show in this work that such longitudinal scans alone can be
used as a form of 'free' self-supervision for training a deep network. We
demonstrate this self-supervised learning for the case of T2-weighted sagittal
lumbar Magnetic Resonance Images (MRIs). A Siamese convolutional neural network
(CNN) is trained using two losses: (i) a contrastive loss on whether the scan
is of the same person (i.e. longitudinal) or not, together with (ii) a
classification loss on predicting the level of vertebral bodies. The
performance of this pre-trained network is then assessed on a grading
classification task. We experiment on a dataset of 1016 subjects, 423
possessing follow-up scans, with the end goal of learning the disc degeneration
radiological gradings attached to the intervertebral discs. We show that the
performance of the pre-trained CNN on the supervised classification task is (i)
superior to that of a network trained from scratch; and (ii) requires far fewer
annotated training samples to reach an equivalent performance to that of the
network trained from scratch.
