发表机构
Iowa State University; Mayo Clinic（爱荷华州立大学; 梅奥诊所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出即插即用的概念干预框架，通过构建概念瓶颈模型实现医学影像模型的可解释调试与细化，在超声和X射线数据集上验证了其诊断可靠性并保持或提升性能。
AI 中文摘要
医学影像模型通常作为黑盒运行，限制了可解释性和系统性调试。我们引入了一个易于使用、即插即用的框架，用于基于概念的解释和模型细化。通过将单模态编码器与BioMedCLIP对齐，我们构建了一个概念瓶颈模型（CBM），该模型支持概念级干预。这些干预使我们能够隔离因果相关概念与虚假相关概念，与领域专家验证见解，并生成反事实样本以进行有针对性的微调。我们在梅奥诊所超声数据集和CheXpert 5x200胸部X射线数据集上评估了我们的框架。结果表明，概念干预能够实现可靠的模型诊断，同时通过引导微调保持并偶尔提升预测性能。我们的发现强调了该框架在临床深度学习模型的可控、可解释细化方面的实用价值。
英文摘要
Medical imaging models often operate as black boxes, limiting interpretability and systematic debugging. We introduce an easy-to-use, plug-and-play framework for concept-based interpretation and model refinement. By aligning a single-modality encoder to BioMedCLIP, we construct a Concept Bottleneck Model (CBM) that enables concept-level interventions. These interventions allow us to isolate causal versus spuriously correlated concepts, validate insights with domain experts, and generate counterfactual samples for targeted fine-tuning. We evaluate our framework on a Mayo Clinic ultrasound dataset and the CheXpert 5x200 chest X-ray dataset. Results demonstrate that concept intervention enables reliable model diagnosis while maintaining, and occasionally improving predictive performance via guided fine-tuning. Our findings highlight the practical value of this framework for controlled, interpretable refinement of clinical deep learning models.
CommentsAccepted at the 5th Workshop on Applications of Medical AI (AMAI), MICCAI 2026
