  An important goal of medical imaging is to be able to precisely detect
patterns of disease specific to individual scans; however, this is challenged
in brain imaging by the degree of heterogeneity of shape and appearance.
Traditional methods, based on image registration to a global template,
historically fail to detect variable features of disease, as they utilise
population-based analyses, suited primarily to studying group-average effects.
In this paper we therefore take advantage of recent developments in generative
deep learning to develop a method for simultaneous classification, or
regression, and feature attribution (FA). Specifically, we explore the use of a
VAE-GAN translation network called ICAM, to explicitly disentangle class
relevant features from background confounds for improved interpretability and
regression of neurological phenotypes. We validate our method on the tasks of
Mini-Mental State Examination (MMSE) cognitive test score prediction for the
Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort, as well as brain age
prediction, for both neurodevelopment and neurodegeneration, using the
developing Human Connectome Project (dHCP) and UK Biobank datasets. We show
that the generated FA maps can be used to explain outlier predictions and
demonstrate that the inclusion of a regression module improves the
disentanglement of the latent space. Our code is freely available on Github
https://github.com/CherBass/ICAM.
