用可学习模糊规则提升GNN可解释性，助力医疗影像分类
FireGNN: Neuro-Symbolic Graph Neural Networks with Trainable Fuzzy Rules for Interpretable Medical Image Classification
- 将可训练模糊规则嵌入GNN，结合节点度、聚类系数等拓扑特征
- 在5个MedMNIST和MorphoMNIST上达到强性能，同时生成规则解释
- 适合需要高可信度解释的临床场景，尤其关注可解释性的研究者
医疗影像分类不仅需高预测性能，还需可解释性以建立临床信任。图神经网络（GNN）能建模数据集中的关系结构，但传统GNN常为黑箱，限制了其在临床中的透明度与可用性。本文提出可解释的图学习框架FireGNN，将可训练模糊规则融入GNN用于医疗影像分类。规则利用可学习的阈值与锐度参数，整合节点度、聚类系数和标签一致性等拓扑描述符，实现内在符号推理。此外，我们引入辅助自监督任务（如同质性预测、相似性熵）作为基准，评估拓扑学习的贡献。所提模型在五个MedMNIST基准和合成数据集MorphoMNIST上表现优异，同时生成可解释的规则化解释。据我们所知，这是首个在GNN中集成可训练模糊规则的工作。
原文摘要 · Abstract (English)
Medical image classification requires not only high predictive performance but also interpretability to ensure clinical trust and adoption. Graph Neural Networks (GNNs) offer a powerful framework for modeling relational structures within datasets; however, standard GNNs often operate as black boxes, limiting transparency and usability, particularly in clinical settings. In this work, we present an interpretable graph-based learning framework named FireGNN that integrates trainable fuzzy rules into GNNs for medical image classification. These rules embed topological descriptors - node degree, clustering coefficient, and label agreement - using learnable thresholds and sharpness parameters to enable intrinsic symbolic reasoning. Additionally, we explore auxiliary self-supervised tasks (e.g., homophily prediction, similarity entropy) as a benchmark to evaluate the contribution of topological learning. Our fuzzy-rule-enhanced model achieves strong performance across five MedMNIST benchmarks and the synthetic dataset MorphoMNIST, while also generating interpretable rule-based explanations. To our knowledge, this is the first integration of trainable fuzzy rules within a GNN. Source Code: https://github.com/basiralab/FireGNN
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