Learning Generative Factors of Neuroimaging Data with Variational auto-encoders. (arXiv:2206.01939v1 [cs.LG])

Neuroimaging techniques produce high-dimensional, stochastic data from which
it might be challenging to extract high-level knowledge about the phenomena of
interest. We address this challenge by applying the framework of generative
modelling to 1) classify multiple pathologies, 2) recover neurological
mechanisms of those pathologies in a data-driven manner and 3) learn robust
representations of neuroimaging data. We illustrate the applicability of the
proposed approach to identifying schizophrenia, either followed or not by
auditory verbal hallucinations. We further demonstrate the ability …

arxiv data learning neuroimaging