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