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Source note3 min read

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

Source

arXiv

arxiv.org/abs/2607.21437v1

Read the source

Source

  • Source name: arXiv
  • Source kind: paper
  • Authors / organization: Nooshin Maghsoodi, Amoon Jamzad, Robert Policelli, Mohammad Farahmand, Dilakshan Srikanthan, Martin Kaufmann, Kevin Y. M. Ren, Shaila Merchant, Sonal Varma, Ross Walker, Doug McKay, John Rudan, Gabor Fichtinger, Parvin Mousavi
  • Venue: arXiv: cs.AI
  • Published: 2026-07-23
  • Collected: 2026-07-24
  • URL: http://arxiv.org/abs/2607.21437v1
  • PDF: https://arxiv.org/pdf/2607.21437v1
  • DOI: UNKNOWN

Plain-Language Brief

Researchers propose Agent-Guided Concept Discovery, a framework that helps deep learning models interpret REIMS data for surgical margin assessment by learning meaningful concepts directly from data without predefined labels. The model improves balanced accuracy and sensitivity over the baseline and shows better generalization to surgical conditions.

What the Document Says

  • Deep learning models can effectively use REIMS data for surgical margin assessment but have difficulty generalizing to operating room conditions due to noisy, unlabeled data acquired during surgery.
  • The black-box nature of deep learning models makes it difficult to understand and improve their behavior.
  • Concept-based learning is a promising way to address these challenges by mapping raw measurements to human-understandable concepts.
  • Supervised concept-based approaches rely on concept annotations, which are difficult to obtain in complex mass spectrometry workflows.
  • The proposed framework learns meaningful concepts directly from data without requiring predefined 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 grounded using a biochemical knowledge graph to ensure consistency with known metabolic relationships.
  • The model improves balanced accuracy and sensitivity over the baseline across Skin and Breast Cancer datasets.
  • In a representative intraoperative case, it shows fewer false positives, indicating better generalization to surgical conditions.

Key Concepts Mentioned

  • Deep learning
  • REIMS data
  • Surgical margin assessment
  • Concept-based learning
  • Agent-Guided Concept Discovery
  • Reasoning agent
  • Biochemical knowledge graph
  • Balanced accuracy
  • Sensitivity
  • Intraoperative case

Claims Made by the Document

  • Deep learning models can effectively use REIMS data for surgical margin assessment but have difficulty generalizing to operating room conditions.
  • Concept-based learning is a promising way to address challenges in deep learning for surgical margin assessment.
  • The proposed Agent-Guided Concept Discovery framework improves balanced accuracy and sensitivity over the baseline across Skin and Breast Cancer datasets.
  • In a representative intraoperative case, the model shows fewer false positives, indicating better generalization to surgical conditions.

Evidence Used

  • The text states that deep learning models can effectively use REIMS data for surgical margin assessment but have difficulty generalizing to operating room conditions.
  • The text mentions concept-based learning as a promising way to address challenges in deep learning for surgical margin assessment and explains the difficulties with supervised concept-based approaches.
  • The text states that the proposed Agent-Guided Concept Discovery framework improves balanced accuracy and sensitivity over the baseline across Skin and Breast Cancer datasets.
  • The text describes a representative intraoperative case where the model shows fewer false positives, indicating better generalization to surgical conditions.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

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.