Metric learning with spectral graph convolutions on brain connectivity networks
… concept of graph convolutions and describe how the filtering operation can be performed
in the graph spectral domain in 2.3. Finally, we describe the proposed network architecture in …
in the graph spectral domain in 2.3. Finally, we describe the proposed network architecture in …
A mutual multi-scale triplet graph convolutional network for classification of brain disorders using functional or structural connectivity
… -scale spatial and topology information of brain connectivity networks, we employ T different
… in this work, as a special form of Laplacian smoothing, convolution operations on each node …
… in this work, as a special form of Laplacian smoothing, convolution operations on each node …
A deep spatiotemporal graph learning architecture for brain connectivity analysis
… convolution operations in the neural network to create a feature representation from the
timeseries belonging to each node. More specifically, we employed 4 layers of 1D Convolution, …
timeseries belonging to each node. More specifically, we employed 4 layers of 1D Convolution, …
Hi-GCN: A hierarchical graph convolution network for graph embedding learning of brain network and brain disorders prediction
… from fMRI brain connectivity networks, where each brain network represents the brain
activity … of jointly learning the graph embedding from both the aspects of the brain functional …
activity … of jointly learning the graph embedding from both the aspects of the brain functional …
Graph convolutional neural networks for brain connectivity analysis
L Jansson, T Sandsröm - 2020 - odr.chalmers.se
… work concerning GNN analysis of brain graphs can be built. … brain connectivity analysis
to other fields where graph … to dense graphs as convolutions performed in the graph domain …
to other fields where graph … to dense graphs as convolutions performed in the graph domain …
Brain functional connectivity analysis via graphical deep learning
… Graph convolution uses the eigenvectors of the Laplacian matrix L in Eq.1 as the bases of
the graph … human cognitive ability and brain connectivity networks by applying the proposed …
the graph … human cognitive ability and brain connectivity networks by applying the proposed …
Predicting brain structural network using functional connectivity
… In order to capture the complex relationship buried in both direct and indirect brain connections…
introduce the notations of a graph and the graph convolution operation used in this work. …
introduce the notations of a graph and the graph convolution operation used in this work. …
Graph saliency maps through spectral convolutional networks: Application to sex classification with brain connectivity
… have been employed to address both graph-centric and node… case, convolutions correspond
to multiplications in the graph … graph convolutions and attention mechanisms for graph- and …
to multiplications in the graph … graph convolutions and attention mechanisms for graph- and …
Similarity learning with higher-order graph convolutions for brain network analysis
… on brain connectivity networks (ie, functional networks) extracted from the input fMRI images,
where the observed brain … networks can be analyzed/modeled using graphtheoretic and …
where the observed brain … networks can be analyzed/modeled using graphtheoretic and …
Spatial–temporal graph convolutional network for Alzheimer classification based on brain functional connectivity imaging of electroencephalogram
… the effect of normal ageing on brain network characteristics before we can accurately diagnose
… Based on the concept of a spectral graph convolution, we introduce the notion of a graph …
… Based on the concept of a spectral graph convolution, we introduce the notion of a graph …
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