  Machine Learning (ML) is increasingly being used for computer aided diagnosis
of brain related disorders based on structural magnetic resonance imaging (MRI)
data. Most of such work employs biologically and medically meaningful
hand-crafted features calculated from different regions of the brain. The
construction of such highly specialized features requires a considerable amount
of time, manual oversight and careful quality control to ensure the absence of
errors in the computational process. Recent advances in Deep Representation
Learning have shown great promise in extracting highly non-linear and
information-rich features from data. In this paper, we present a novel
large-scale deep unsupervised approach to learn generic feature representations
of structural brain MRI scans, which requires no specialized domain knowledge
or manual intervention. Our method produces low-dimensional representations of
brain structure, which can be used to reconstruct brain images with very low
error and exhibit performance comparable to FreeSurfer features on various
classification tasks.
