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Graph Saliency Maps through Spectral Convolutional Networks: Application to Sex Classification with Brain Connectivity
[article]
2018
arXiv
pre-print
Graph convolutional networks (GCNs) allow to apply traditional convolution operations in non-Euclidean domains, where data are commonly modelled as irregular graphs. Medical imaging and, in particular, neuroscience studies often rely on such graph representations, with brain connectivity networks being a characteristic example, while ultimately seeking the locus of phenotypic or disease-related differences in the brain. These regions of interest (ROIs) are, then, considered to be closely
arXiv:1806.01764v1
fatcat:orqgczzaxzg75b4tup2dca5epq