  Arguably, unsupervised learning plays a crucial role in the majority of
algorithms for processing brain imaging. A recently introduced unsupervised
approach Deep InfoMax (DIM) is a promising tool for exploring brain structure
in a flexible non-linear way. In this paper, we investigate the use of variants
of DIM in a setting of progression to Alzheimer's disease in comparison with
supervised AlexNet and ResNet inspired convolutional neural networks. As a
benchmark, we use a classification task between four groups: patients with
stable, and progressive mild cognitive impairment (MCI), with Alzheimer's
disease, and healthy controls. Our dataset is comprised of 828 subjects from
the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Our
experiments highlight encouraging evidence of the high potential utility of DIM
in future neuroimaging studies.
