通过因果发现与曲率优化，构建更精准的脑区连接图以提升疾病分类效果。
Refined Causal Graph Structure Learning via Curvature for Brain Disease Classification
- 基于转移熵与几何曲率，挖掘脑区间的因果关系
- 在多个数据集上平均F1得分超越现有方法
- 适合脑科学与医学影像分析研究者使用
图神经网络（GNN）被用于建模大脑感兴趣区域（ROIs）之间的关系，在检测脑疾病方面表现显著。然而，大多数框架未考虑脑区间因果关系这一关键因素，而因果关系更能揭示信号间的因果互动而非简单相关性。本文提出一种名为CGB（Causal Graphs for Brains）的新框架，通过因果发现方法（转移熵）与几何曲率策略，构建精细化的脑网络结构。CGB揭示了脑区间潜在的因果联系，为疾病分类提供关键信息；同时，利用几何曲率对生成的因果图进行重连路优化，增强其表达能力，并减少GNN建模时的信息瓶颈。大量实验表明，CGB在脑疾病分类任务中优于当前先进方法，平均F1得分更高。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。