Similarity Learning with Higher-Order Graph Convolutions for Brain Network Analysis

Understand

Learning a similarity metric has gained much attention recently, where the goal is to learn a function that maps input patterns to a target space while preserving the semantic distance in the input space.

• While most related work focused on images, we focus instead on learning a similarity metric for neuroimages, such as fMRI and DTI images.
• We propose an end-to-end similarity learning framework called Higher-order Siamese GCN for multi-subject fMRI data analysis.
• The proposed framework learns the brain network representations via a supervised metric-based approach with siamese neural networks using two graph convolutional networks as the twin networks.