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.
  • IEEE MemberUS $11.00
  • Society MemberUS $0.00
  • IEEE Student MemberUS $11.00
  • Non-IEEE MemberUS $15.00
Purchase

Videos in this product

Multi-resolution Graph Neural Network for Detecting Variations in Brain Connectivity

00:06:48
1 view
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.