NeRD：面向医学图像诊断的高效本体接地思维链的神经符号规则蒸馏
NeRD: Neuro-Symbolic Rule Distillation for Efficient Ontology-Grounded Chain-of-Thought in Medical Image Diagnosis
- Department of Data Science & AI, Faculty of Information Technology, Monash University（莫纳什大学信息技术学院数据科学与人工智能系）
- AIM for Health Lab, Faculty of Information Technology, Monash University（莫纳什大学信息技术学院AIM健康实验室）
- Faculty of Engineering, Monash University（莫纳什大学工程学院）
- Faculty of Medicine, The Chinese University of Hong Kong（香港中文大学医学院）
- School of Computing Technologies, RMIT University（皇家墨尔本理工大学计算技术学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出NeRD框架，通过神经符号规则蒸馏生成高效、本体接地且非冗余的推理链，避免人工规则，在皮肤数据集上实现强诊断性能和可解释性，并首次实现专家介入的多模态思维链诊断。
AI中文摘要：
可解释性对于可信的医学图像诊断至关重要。然而，现有的概念驱动可解释方法存在关键局限性：概念瓶颈模型（CBM）需要在推理时对所有预定义概念进行评分并用于人工干预，给临床医生带来沉重负担；而基于理由的生成方法通常通过类别可区分性选择概念，这可能偏离诊断本体。为了解决这些问题，我们提出了神经符号规则蒸馏（NeRD），这是一个生成高效、本体接地且充分而非冗余的推理链的框架，无需手动构建诊断规则。在两个皮肤数据集上的实验证明了其强大的诊断性能和可解释性，盲法专家评估确认了NeRD理由的临床合理性。我们的方法进一步实现了首次专家介入的多模态思维链诊断研究，实现了高效且有效的概念级干预。
英文摘要：
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.
