这篇论文用智能体自动发现概念，让REIMS数据的手术切缘评估更可解释、更准确，真实术中场景也表现更好。
本研究提出Agent-Guided Concept Discovery框架，用于从REIMS质谱数据中学习可解释概念以辅助手术切缘评估。该框架无需预定义概念标签，通过推理代理优化语义描述并利用生化知识图谱确保一致性。在皮肤癌和乳腺癌数据集上，该方法相较于基线提高了平衡准确率和灵敏度。在术中案例中，假阳性更少，表明更好的泛化能力。
Agent-Guided Relational Concept Discovery: Toward Interpretable Surgical Margin Assessment
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.