XG-GNN

Description

XG-GNN, developed through a specialized meta-learning framework with explainability-generalizable (XG) regularizations, learns diagnostic GNN models from fMRI BOLD signals. It features the ability to build nonlinear functional networks in a task-oriented fashion. More importantly, the group-wise differences of such learned networks can be stably captured and maintained to unseen fMRI centers to jointly boost the DG of diagnostic explainability and accuracy. Experimental results on the ABIDE dataset demonstrate the effectiveness of our XG-GNN.