用符号规则蒸馏让医学影像诊断更准确且可解释。
NeRD: Neuro-Symbolic Rule Distillation for Efficient Ontology-Grounded Chain-of-Thought in Medical Image Diagnosis
- 用神经符号方法从模型中提取符合医学本体的推理规则。
- 在两个皮肤数据集上表现优异，专家评估认可其推理合理性。
- 支持医生介入修改概念，适合临床协作场景。
可解释性对可信的医学影像诊断至关重要。现有基于概念的可解释方法存在关键缺陷：概念瓶颈模型（CBMs）需在推理时评分所有预定义概念并依赖人工干预，给临床带来沉重负担；而基于理由生成的方法常依据类别区分度选择概念，易偏离诊断本体。为此，我们提出神经符号规则蒸馏（NeRD），构建高效、本体对齐的推理链，无需手动设计诊断规则。在两个皮肤疾病数据集上的实验表明，该方法兼具强诊断性能与可解释性；盲评专家确认其推理过程具有临床合理性。此外，该方法首次实现多模态思维链诊断中的专家在环研究，支持高效的概念级干预。
原文摘要 · Abstract (English)
Interpretability is essential for trustworthy medical image diagnosis. However, existing concept-driven interpretable methods have key limitations: Concept Bottleneck Models (CBMs) require scoring all predefined concepts at inference time and for manual intervention, imposing a substantial burden on clinicians, while rationale-based generative approaches often select concepts by class discriminability, which can drift from diagnostic ontologies. To address these issues, we propose Neuro-Symbolic Rule Distillation (NeRD), a framework that produces efficient, ontology-grounded reasoning chains that are sufficient yet non-redundant, without manually crafting diagnostic rules. Experiments on two skin datasets demonstrate strong diagnostic performance and interpretability, and blinded expert evaluation confirms the clinical plausibility of NeRD rationales. Our method further enables a first expert-in-the-loop study for Multimodal Chain-of-Thought-based diagnosis, achieving efficient and effective concept-level intervention.
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