用可解释规则分析脑连接数据，准确识别阿尔茨海默病
LearnAD: Learning Interpretable Rules for Brain Networks in Alzheimer's Disease Classification
- 结合统计模型与符号学习，从脑影像中提取可解释规则
- 准确率接近随机森林和GNN，且保持完全可解释性
- 适合关注模型可解释性的临床神经科学研究者
我们提出LearnAD，一种用于从脑磁共振成像数据中预测阿尔茨海默病的神经符号方法，能够学习完全可解释的规则。LearnAD使用统计模型、决策树、随机森林或图神经网络（GNNs）识别相关脑连接，再通过FastLAS学习全局规则。最佳实例在准确率上优于决策树，达到支持向量机水平，仅略低于使用全部特征训练的随机森林和GNN，同时保持完全可解释性。消融实验表明，该神经符号方法在性能相近的前提下显著提升可解释性。结果展示了符号学习如何深化对GNN在临床神经科学中行为的理解。
原文摘要 · Abstract (English)
We introduce LearnAD, a neuro-symbolic method for predicting Alzheimer's disease from brain magnetic resonance imaging data, learning fully interpretable rules. LearnAD applies statistical models, Decision Trees, Random Forests, or GNNs to identify relevant brain connections, and then employs FastLAS to learn global rules. Our best instance outperforms Decision Trees, matches Support Vector Machine accuracy, and performs only slightly below Random Forests and GNNs trained on all features, all while remaining fully interpretable. Ablation studies show that our neuro-symbolic approach improves interpretability with comparable performance to pure statistical models. LearnAD demonstrates how symbolic learning can deepen our understanding of GNN behaviour in clinical neuroscience.
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