发表机构
Queen’s University（女王大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对深度学习模型用于手术切缘评估时面临的泛化难及黑箱问题，提出代理引导的概念发现框架，直接从数据学习概念，经推理代理和知识图谱完善调整，在相关数据集和术中病例中提升了评估效果。
AI 中文摘要
深度学习模型可有效利用快速蒸发电离质谱（REIMS）数据进行手术切缘评估。但由于对手术室条件的泛化能力有限，其临床应用仍具挑战。模型通常在切除组织样本的标记光谱上训练，却要处理手术中直接获取的噪声、未标记数据。此外，深度学习模型的黑箱性质使其行为难以理解和系统改进。基于概念的学习有望解决这些问题，但监督式基于概念的方法依赖概念注释，在复杂质谱工作流程中难以获取。我们提出代理引导的概念发现框架，可直接从数据中学习有意义的概念，无需预定义概念标签。训练时，推理代理完善所学概念的语义描述，并根据诊断相关性自适应调整其权重。这些概念通过生化知识图谱进一步落地，以确保与已知代谢关系一致。在皮肤癌和乳腺癌数据集上，我们的模型比基线提高了平衡准确率和灵敏度。在一个有代表性的术中病例中，它显示出更少的假阳性，表明对手术条件有更好的泛化能力。
英文摘要
Deep learning models can effectively use Rapid Evaporative Ionization Mass Spectrometry (REIMS) data for surgical margin assessment. However, their clinical adoption remains challenging due to limited generalization to operating room conditions. This difficulty arises because models are typically trained on labeled spectra collected from resected tissue samples, while they must operate on noisy, unlabeled data acquired directly during surgery. In addition, the black-box nature of deep learning models makes it difficult to understand and systematically improve their behavior. Concept-based learning offers a promising way to address these challenges by mapping raw measurements to human-understandable concepts. However, supervised concept-based approaches rely on concept annotations, which are difficult to obtain in complex mass spectrometry workflows. We propose Agent-Guided Concept Discovery, a framework that learns meaningful concepts directly from data without requiring predefined concept labels. During training, a reasoning agent refines semantic descriptions of the learned concepts and adaptively adjusts their weight based on diagnostic relevance. These concepts are further grounded using a biochemical knowledge graph to ensure consistency with known metabolic relationships. Across Skin and Breast Cancer datasets, our model improves balanced accuracy and sensitivity over the baseline. In a representative intraoperative case, it shows fewer false positives, indicating better generalization to surgical conditions.
CommentsThis paper is accepted to MICCAI 2026, and this is the submission version, not the camera-ready version
