Learning Generative Factors of Neuroimaging Data with Variational auto-encoders. (arXiv:2206.01939v2 [cs.LG] UPDATED)

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 generative modelling
framework to 1) classify multiple pathologies and 2) recover the neurological
mechanisms of those pathologies in a data-driven manner. Our framework learns
generative factors of data related to pathologies. We provide an algorithm to
decode those factors further and observe how different pathologies affect
observed data. We illustrate the applicability …

arxiv data learning lg neuroimaging