Agent-Guided Relational Concept Discovery: Toward Interpretable Surgical Margin Assessment

TL;DR - Agent-Guided Concept Discovery interprets REIMS surgical-margin data by learning concepts without manual annotations and grounding them in biochemical knowledge. It improves cancer-classification performance and shows better intraoperative generalization.

• A reasoning agent refines concept descriptions and weights them by diagnostic relevance.
• A biochemical knowledge graph aligns learned concepts with known metabolic relationships.
• The model improves balanced accuracy and sensitivity on skin and breast cancer datasets versus the baseline.
• In one representative intraoperative case, it produces fewer false positives.