Spatially Grounded Concept Bottleneck Models for Trustworthy Breast Ultrasound Diagnosis

2026-07-22

AI Summary

This study addresses the unreliability of explanations in conventional Concept Bottleneck Models (CBMs) for breast ultrasound diagnosis, which arises from misaligned concept activations due to the absence of spatial supervision. To resolve this, the authors propose the Spatially Guided Concept Bottleneck Model (SG-CBM), which introduces lesion masks partitioned into intra-lesion and posterior acoustic shadow regions. Leveraging coarse lesion contours as weak spatial supervision, SG-CBM employs grouped spatial anchoring objectives to guide clinically relevant concepts—such as morphology and posterior acoustic shadowing—to activate at anatomically plausible locations. A linear bottleneck classifier is integrated to jointly preserve semantic fidelity and spatial alignment. Experiments demonstrate that SG-CBM significantly improves both diagnostic AUROC and macro concept AUROC, while markedly enhancing the spatial alignment of concept evidence, thereby underscoring the critical role of high-quality spatial supervision in developing trustworthy medical AI systems.

Abstract

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.

Problem

• Concept Bottleneck Models
• spatial faithfulness
• medical imaging
• breast ultrasound
• trustworthy AI

Innovation

• Concept Bottleneck Models
• Spatial Grounding
• Weak Supervision
• Breast Ultrasound
• Interpretable AI