Refined Causal Graph Structure Learning via Curvature for Brain Disease Classification
This work addresses brain disease detection for medical applications by enhancing classification accuracy, though it appears incremental as it builds on existing GNN and causal discovery techniques.
The authors tackled brain disease classification by proposing a framework that models causal relationships between brain regions and refines the graph structure using geometric curvature, achieving improved performance over state-of-the-art methods as measured by average F1 scores.
Graph neural networks (GNNs) have been developed to model the relationship between regions of interest (ROIs) in brains and have shown significant improvement in detecting brain diseases. However, most of these frameworks do not consider the intrinsic relationship of causality factor between brain ROIs, which is arguably more essential to observe cause and effect interaction between signals rather than typical correlation values. We propose a novel framework called CGB (Causal Graphs for Brains) for brain disease classification/detection, which models refined brain networks based on the causal discovery method, transfer entropy, and geometric curvature strategy. CGB unveils causal relationships between ROIs that bring vital information to enhance brain disease classification performance. Furthermore, CGB also performs a graph rewiring through a geometric curvature strategy to refine the generated causal graph to become more expressive and reduce potential information bottlenecks when GNNs model it. Our extensive experiments show that CGB outperforms state-of-the-art methods in classification tasks on brain disease datasets, as measured by average F1 scores.