通过学习二值化概念的逻辑规则，提升医学影像分类的可解释性与泛化能力。
Learning Concept-Driven Logical Rules for Interpretable and Generalizable Medical Image Classification
- 基于二值化视觉概念构建布尔逻辑规则，捕捉概念间关联
- 在两个任务上实现与现有方法相当的性能，且对分布外数据泛化更强
- 同时提供个体实例和整体数据集层面的可解释性，适合临床决策支持
临床应用中的决策安全性推动了基于概念的医学影像方法发展。尽管这些模型具备主动可解释性，但常因软概念表示中混入无关信息而出现概念泄露，损害可解释性与泛化能力。此外，多数方法仅关注局部解释（实例级），忽视全局决策逻辑（数据集级）。为此，我们提出概念规则学习器（CRL），从二值化视觉概念中学习布尔逻辑规则。CRL采用逻辑层捕获概念相关性，提取具有临床意义的规则，从而实现局部与全局双重可解释性。在两项医学图像分类任务上的实验表明，CRL在性能上与现有方法相当，且显著提升对分布外数据的泛化能力。代码已开源：https://github.com/obiyoag/crl。
原文摘要 · Abstract (English)
The pursuit of decision safety in clinical applications highlights the potential of concept-based methods in medical imaging. While these models offer active interpretability, they often suffer from concept leakages, where unintended information within soft concept representations undermines both interpretability and generalizability. Moreover, most concept-based models focus solely on local explanations (instance-level), neglecting the global decision logic (dataset-level). To address these limitations, we propose Concept Rule Learner (CRL), a novel framework to learn Boolean logical rules from binarized visual concepts. CRL employs logical layers to capture concept correlations and extract clinically meaningful rules, thereby providing both local and global interpretability. Experiments on two medical image classification tasks show that CRL achieves competitive performance with existing methods while significantly improving generalizability to out-of-distribution data. The code of our work is available at https://github.com/obiyoag/crl.
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