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Multi-resolution Graph Neural Network for Detecting Variations in Brain Connectivity
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Multi-resolution Graph Neural Network for Detecting Variations in Brain Connectivity
In this work, we propose a novel CNN-based framework with adaptive graph transforms to learn the most disease-relevant connectome feature maps which have the highest discrimination power across-diagnostic categories. The backbone of our framework is a multi-resolution representation of the graph matrix which is steered by a set of wavelet-like graph transforms. Our graph learning framework outperforms conventional methods that predict diagnostic label for graphs.
In this work, we propose a novel CNN-based framework with adaptive graph transforms to learn the most disease-relevant connectome feature maps which have the highest discrimination power across-diagnostic categories. The backbone of our framework is a multi-resolution representation of the graph matrix which is steered by a set of wavelet-like graph transforms. Our graph learning framework outperforms conventional methods that predict diagnostic label for graphs.
