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An important goal of medical imaging is to be able to precisely detectpatterns of disease specific to individual scans; however, this is challengedin 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 utilisepopulation-based analyses, suited primarily to studying group-average effects.In this paper we therefore take advantage of recent developments in generativedeep learning to develop a method for simultaneous classification, orregression, and feature attribution (FA). Specifically, we explore the use of aVAE-GAN translation network called ICAM, to explicitly disentangle classrelevant features from background confounds for improved interpretability andregression of neurological phenotypes. We validate our method on the tasks ofMini-Mental State Examination (MMSE) cognitive test score prediction for theAlzheimer's Disease Neuroimaging Initiative (ADNI) cohort, as well as brain ageprediction, for both neurodevelopment and neurodegeneration, using thedeveloping Human Connectome Project (dHCP) and UK Biobank datasets. We showthat the generated FA maps can be used to explain outlier predictions anddemonstrate that the inclusion of a regression module improves thedisentanglement of the latent space. Our code is freely available on Githubhttps://github.com/CherBass/ICAM.

ICAM-reg: Interpretable Classification and Regression with Feature Attribution for Mapping Neurological Phenotypes in Individual Scans