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

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 a new framework called Agent-Guided Concept Discovery for improving deep learning models used in surgical margin assessment. The model learns meaningful concepts directly from data without requiring predefined labels and can improve accuracy and sensitivity over the baseline, especially in generalizing to surgical conditions.

What the Document Says

Deep learning models are effective for surgical margin assessment using REIMS data.
The clinical adoption of these models is challenging due to limited generalization to operating room conditions.
The models are typically trained on labeled spectra from resected tissue samples but must operate on 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 solution for addressing these challenges by mapping raw measurements to human-understandable concepts.
Supervised concept-based approaches rely on concept annotations, which are hard to obtain in complex mass spectrometry workflows.
The proposed framework (Agent-Guided Concept Discovery) learns meaningful concepts directly from data without requiring predefined labels.
A reasoning agent refines semantic descriptions of the learned concepts and adjusts their weight based on diagnostic relevance.
The concepts are grounded using a biochemical knowledge graph to ensure consistency with known metabolic relationships.
The model shows improvements in 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 models
Rapid Evaporative Ionization Mass Spectrometry (REIMS)
Surgical margin assessment
Concept-based learning
Agent-Guided Concept Discovery
Reasoning agent
Semantic descriptions
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 limited generalization to operating room conditions.
The proposed Agent-Guided Concept Discovery framework learns meaningful concepts directly from data without requiring predefined labels.
The new model 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.

Evidence Used

Deep learning models can effectively use REIMS data for surgical margin assessment. However, their clinical adoption remains challenging due to limited generalization to operating room conditions.

Methods / Approach

The researchers propose Agent-Guided Concept Discovery framework that learns meaningful concepts directly from data without requiring predefined labels.
A reasoning agent refines semantic descriptions of the learned concepts and adjusts their weight based on diagnostic relevance.
The concepts are grounded using a biochemical knowledge graph.

Limitations Stated by the Authors

Supervised concept-based approaches rely on concept annotations, which are difficult to obtain in complex mass spectrometry workflows.

Limitations Noted by the Agent

Agent interpretation — not from the source.

The study does not provide information about the specific deep learning model used.
The research focuses on Skin and Breast Cancer datasets; it's unclear how well the model performs on other types of cancers.

Open Questions

What is the specific deep learning model used in this study?
How does the Agent-Guided Concept Discovery perform on other types of cancers?

Notes for Future Q&A

May include interpretation. Labeled where it does.

The text provides no information about the specific deep learning model employed.
The research focuses solely on Skin and Breast Cancer datasets.

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.