Deepa Tilwani will deliver the talk.
One of the most common paradigms for neuroimaging analysis is the mapping of the human connectome utilizing structural or functional connectivity. Due to their proven ability to represent complicated networked data, Graph Neural Networks (GNNs), motivated by geometric deep learning, have recently gained a lot of attention. The best way to create efficient GNNs for brain network research has not yet been thoroughly studied, despite their better performance in many disciplines. This work provides a benchmark for brain network analysis with GNNs, to fill this gap by summarizing the pipelines for building brain networks for both structural and functional neuroimaging modalities and by modularizing the execution of GNN designs. Overview Paper.
Meeting ID: 860 1921 3021