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A Heterogeneous Graph Based Framework for Multimodal Neuroimaging Fusion Learning. (arXiv:2110.08465v4 [cs.LG] UPDATED)
cs.LG updates on arXiv.org arxiv.org
Graph neural networks (GNNs) provide powerful insights for brain neuroimaging
technology from the view of graphical networks. However, most existing
GNN-based models assume that the neuroimaging-produced brain connectome network
is a homogeneous graph with single types of nodes and edges. In fact, emerging
studies have reported and emphasized the significance of heterogeneity among
human brain activities, especially between the two cerebral hemispheres. Thus,
homogeneous-structured brain network-based graph methods are insufficient for
modelling complicated cerebral activity states. To overcome this problem, …
arxiv framework fusion graph learning lg multimodal neuroimaging