Latent graph encoding of multimodal neuroimaging features with generative AI architectures

arXiv:2607.07027v1 Announce Type: new Abstract: While generative models enable encoding of complex neuroimaging data for feature generation and reconstruction, developing optimal architectural frameworks with appropriate encoding and latent space processes is crucial for studying structural and functional properties of the brain. We design a multimodal generative framework for structural and functional magnetic resonance imaging (MRI) features through systematic evaluation of encoding strategies, latent multimodal fusion, and generative model selection. Using structural gray matter volume (GMV

Source: arXiv cs.LG — read the full report at the original publisher.

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