Multimodal and multicontrast image fusion via deep generative models

arXiv:2303.15963v2 Announce Type: replace
Abstract: Recently, it has become progressively more evident that classic diagnostic labels are unable to reliably describe the complexity and variability of several clinical phenotypes. This is particularly true for a broad range of neuropsychiatric illnesses (e.g., depression, anxiety disorders, behavioral phenotypes). Patient heterogeneity can be better described by grouping individuals into novel categories based on empirically derived sections of intersecting continua that span across and beyond traditional categorical borders. In this context, neuroimaging data carry …

abstract anxiety arxiv become clinical complexity cs.lg deep generative models depression diagnostic fusion generative generative models image labels multimodal patient true type via