  While unsupervised variational autoencoders (VAE) have become a powerful tool
in neuroimage analysis, their application to supervised learning is
under-explored. We aim to close this gap by proposing a unified probabilistic
model for learning the latent space of imaging data and performing supervised
regression. Based on recent advances in learning disentangled representations,
the novel generative process explicitly models the conditional distribution of
latent representations with respect to the regression target variable.
Performing a variational inference procedure on this model leads to joint
regularization between the VAE and a neural-network regressor. In predicting
the age of 245 subjects from their structural Magnetic Resonance (MR) images,
our model is more accurate than state-of-the-art methods when applied to either
region-of-interest (ROI) measurements or raw 3D volume images. More
importantly, unlike simple feed-forward neural-networks, disentanglement of age
in latent representations allows for intuitive interpretation of the structural
developmental patterns of the human brain.
