AI 中文总结
研究乳腺超声诊断中概念瓶颈模型可信度受监督限制问题，提出空间基础概念瓶颈模型（SG-CBM），利用病变轮廓弱监督，通过导出特定区域训练概念图，经交叉验证等提升诊断指标与概念证据空间对齐，强调数据质量监督设计及可信度验证的必要。
AI 中文摘要
概念瓶颈模型通过人类可理解的概念进行诊断，从而提供可解释的预测。但在医学成像中，其可信度常受可用监督质量和粒度的限制。特别是预测的概念激活可能由无关区域驱动，导致空间上不忠实的解释。我们研究了一种以数据为中心的空间基础概念瓶颈模型（SG-CBM），利用粗略的病变轮廓作为弱监督，以鼓励解剖学上合理的概念证据。对于乳腺超声，我们从每个病变掩码中导出两个临床相关区域：一个用于形态学相关概念的病变内感兴趣区域，另一个用于后方现象的后方声束区域。我们使用分组空间基础目标训练概念图，并通过线性瓶颈分类器保持语义忠实性。在五折分层组交叉验证中，所提出的SG-CBM提高了诊断AUROC和概念宏AUROC，同时显著增加了概念证据的空间对齐。我们还进行了训练-损坏/测试-清洁注释质量压力测试，以量化监督质量对诊断和空间忠实性的影响。总体而言，结果强调了对可部署医疗保健人工智能系统进行数据质量感知监督设计和系统可信度验证的必要性。
英文摘要
Concept Bottleneck Models provide interpretable-by-design predictions by mediating diagnosis through human-understandable concepts, but in medical imaging, their trustworthiness is often limited by the quality and granularity of available supervision. In particular, predicted concept activations can be driven by irrelevant regions, leading to spatially unfaithful explanations. We study a data-centric spatially grounded Concept Bottleneck Model (SG-CBM) that leverages coarse lesion delineations as weak supervision to encourage anatomically plausible concept evidence. For breast ultrasound, we derive two clinically motivated zones from each lesion mask: (i) an in-lesion region of interest for morphology-related concepts and (ii) a posterior acoustic band for posterior phenomena. We train concept maps using a grouped spatial grounding objective and preserve semantic faithfulness with a linear bottleneck classifier. Across five-fold stratified group cross-validation, the proposed SG-CBM improves diagnostic AUROC and concept macro-AUROC while markedly increasing spatial alignment of concept evidence. We also perform a Train-corrupt/Test-clean annotation-quality stress test to quantify the impact of supervision quality on diagnosis and spatial faithfulness. Overall, the results underscore the need for data-quality-aware supervision design and systematic trustworthiness validation for deployable healthcare AI systems.
CommentsAccepted to the Workshop on Data Quality Aware, High-Performance, and Trustworthy AI Systems for Healthcare at IEEE/ACM CHASE 2026
