InVA: Integrative Variational Autoencoder for Harmonization of Multi-modal Neuroimaging Data

There is a significant interest in exploring non-linear associations among multiple images derived from diverse imaging modalities. While there is a growing literature on image-on-image regression to delineate predictive inference of an image based on multiple images, existing approaches have limitations in efficiently borrowing information between multiple imaging modalities in the prediction of an image. Building on the literature of Variational Auto Encoders (VAEs), this article proposes a novel approach, referred to as Integrative Variational Autoencoder (\texttt{InVA}) method, which borrows …

autoencoder cs.cv cs.ne data diverse image images imaging inference information limitations linear literature modal multi-modal multiple neuroimaging non-linear predictive regression stat.ap stat.ml