A graph-attention-based multimodal VAE achieves competitive reconstruction and generation of structural and functional MRI features while being more computationally efficient than diffusion-based alternatives.
Comprehensive evaluation on a large dataset demonstrates that graph-based generative models substantially outperform vectorized approaches
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Latent graph encoding of multimodal neuroimaging features with generative AI architectures
A graph-attention-based multimodal VAE achieves competitive reconstruction and generation of structural and functional MRI features while being more computationally efficient than diffusion-based alternatives.