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Agent-Guided Relational Concept Discovery: Toward I... | AI Research

Key Takeaways

  • Agent-Guided Relational Concept Discovery: Toward Interpretable Surgical Margin Assessment addresses a critical challenge in surgical oncology: using Rapid E...
  • 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.
Paper AbstractExpand

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.

Agent-Guided Relational Concept Discovery: Toward Interpretable Surgical Margin Assessment addresses a critical challenge in surgical oncology: using Rapid Evaporative Ionization Mass Spectrometry (REIMS) to identify cancer cells in real-time during surgery. While deep learning models can classify tissue types using REIMS data, they often struggle to generalize from controlled lab environments to the noisy, unpredictable conditions of an operating room. Furthermore, these models typically function as "black boxes," making it difficult for surgeons to understand the biological evidence behind a diagnosis. This research introduces a framework that learns human-understandable concepts directly from data, grounding them in biochemical knowledge to improve both the accuracy and the transparency of surgical margin assessments.

Learning Concepts Without Manual Labels

Traditional concept-based learning requires experts to manually annotate concepts, which is impractical in complex mass spectrometry workflows. This new framework bypasses that requirement by using a "reasoning agent" integrated directly into the training loop. The model automatically discovers discriminative spectral patterns—the "concepts"—from the data. As the model trains, the reasoning agent analyzes these patterns, assigns them semantic descriptions, and determines which concepts are most relevant for accurate diagnosis. This allows the model to learn meaningful, high-level representations of tissue without needing human-provided labels for every concept.

Grounding Decisions in Biochemical Knowledge

To ensure the model’s "reasoning" aligns with medical reality, the framework connects learned concepts to a biochemical knowledge graph. The reasoning agent queries metabolic databases to link specific spectral features to known metabolites and pathways. By maintaining this knowledge graph, the model ensures that its internal logic is consistent with established biological relationships. During training, the agent provides feedback that adjusts the weight of each concept, forcing the model to rely more heavily on clinically significant biological evidence and less on noise or irrelevant data.

Improved Performance and Generalization

The framework was tested on both skin and breast cancer datasets. Compared to baseline models, the agent-guided approach demonstrated improved balanced accuracy and sensitivity. In a real-world intraoperative breast cancer case, the model showed fewer false positives than standard approaches, particularly when distinguishing between tumor tissue and skin. This suggests that by focusing on biologically aware concepts, the model is more robust to the variations encountered during actual surgery, helping surgeons make more reliable decisions at the point of incision.

Future Directions

While the current model shows significant promise in bridging the gap between lab-based training and clinical application, it currently relies on general metabolic database queries. The researchers note that future work will focus on more targeted querying strategies and the integration of gene-level information. These advancements aim to further refine the model's biological specificity, making the diagnostic process even more precise and interpretable for clinical use.

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