  Developing successful artificial intelligence systems in practice depends on
both robust deep learning models and large, high-quality data. However,
acquiring and labeling data can be prohibitively expensive and time-consuming
in many real-world applications, such as clinical disease models.
Self-supervised learning has demonstrated great potential in increasing model
accuracy and robustness in small data regimes. In addition, many clinical
imaging and disease modeling applications rely heavily on regression of
continuous quantities. However, the applicability of self-supervised learning
for these medical-imaging regression tasks has not been extensively studied. In
this study, we develop a cross-domain self-supervised learning approach for
disease prognostic modeling as a regression problem using medical images as
input. We demonstrate that self-supervised pretraining can improve the
prediction of Alzheimer's Disease progression from brain MRI. We also show that
pretraining on extended (but not labeled) brain MRI data outperforms
pretraining on natural images. We further observe that the highest performance
is achieved when both natural images and extended brain-MRI data are used for
pretraining.
