Cross-Model Agreement as a Deployment-Time Reliability Signal for Automatic Polyp Segmentation
Proposed RBQE framework uses cross-model agreement to estimate polyp segmentation reliability, achieving ROC-AUC of 0.960.
Key Findings
Methodology
RBQE evaluates segmentation reliability by measuring agreement between a primary segmentation model and an independently trained referee model. Four referee configurations were tested: same-architecture independent training, cross-architecture independent training (SegFormer-B0 and UNet++), and prompt-based MedSAM.
Key Results
- Result 1: SegFormer-B0 referee achieved ROC-AUC of 0.960, significantly outperforming the same-architecture referee (0.923) and test-time augmentation baseline (+0.055).
- Result 2: Excluding empty masks, SegFormer-B0 achieved ROC-AUC of 0.876, maintaining superiority over baselines with a widened performance margin.
- Result 3: RBQE increased the mean Dice of retained predictions as low-agreement cases were rejected, supporting selective prediction.
Significance
RBQE provides a practical, interpretable framework for assessing polyp segmentation reliability in real-time colonoscopy without requiring ground-truth annotations. It addresses the critical challenge of silent segmentation failures, enabling safer clinical adoption of automated systems.
Technical Contribution
RBQE systematically validates cross-architecture, independently trained model agreement as a reliability signal for segmentation. It introduces five agreement descriptors (e.g., Agreement Dice, Boundary Agreement) and demonstrates significant improvements over state-of-the-art baselines in reference-free quality estimation.
Novelty
RBQE is the first to leverage cross-architecture model agreement for segmentation quality estimation, offering improved generalization and interpretability compared to single-model uncertainty or same-architecture ensembles.
Limitations
- Limitation 1: Handling of empty masks may oversimplify certain cases, potentially affecting real-world performance.
- Limitation 2: Results are sensitive to the choice of referee models, requiring task-specific tuning.
- Limitation 3: The framework has not been validated on larger or more diverse real-world datasets.
Future Work
Future work could explore more advanced referee configurations, validate RBQE on broader medical imaging tasks, and integrate it with other reference-free quality estimation techniques.
AI Executive Summary
Colorectal cancer is the second leading cause of cancer-related deaths globally, with early detection of polyps being critical for improving survival rates. However, automated polyp segmentation models often fail silently under challenging conditions, lacking reliable quality estimation mechanisms during inference.
To address this, the authors propose Referee-Based Quality Estimation (RBQE), a novel framework that evaluates segmentation reliability by measuring agreement between a primary segmentation model and an independently trained referee model. Four referee configurations were tested, with the cross-architecture SegFormer-B0 achieving the highest ROC-AUC of 0.960, significantly outperforming baselines like test-time augmentation.
RBQE is a practical, label-free framework requiring only a single deterministic forward pass of a referee model. It is versatile and interpretable, making it a promising tool for real-time clinical applications. Future research could focus on optimizing referee models and extending RBQE to other medical imaging tasks.
Deep Analysis
Background
Colorectal cancer is a major global health issue, with early polyp detection through colonoscopy being critical. While deep learning has advanced polyp segmentation, models often fail under challenging conditions like poor illumination or domain shifts, lacking mechanisms to assess reliability during inference.
Core Problem
Existing segmentation models can silently fail during inference, especially under challenging conditions. Current quality estimation methods rely on single-model uncertainty or same-architecture ensembles, which are prone to calibration errors and shared biases, limiting their reliability in real-world applications.
Innovation
RBQE introduces a novel framework that evaluates segmentation reliability using cross-model agreement. Key innovations include: • Systematic validation of cross-architecture model agreement as a reliability signal. • Introduction of five agreement descriptors (e.g., Agreement Dice, Boundary Agreement). • A practical, deployment-ready framework requiring no retraining or ground-truth annotations.
Methodology
- �� Generate segmentation masks using a primary model and an independently trained referee model.
- �� Quantify agreement using descriptors like Agreement Dice, Boundary Agreement, and Centroid Distance.
- �� Evaluate reliability based on agreement scores.
- �� Test four referee configurations: same-architecture independent training, cross-architecture independent training (SegFormer-B0, UNet++), and prompt-based MedSAM.
Experiments
The framework was evaluated on four external datasets (CVC-ClinicDB, CVC-ColonDB, ETIS-Larib PolypDB, CVC-300) totaling 1,223 images. ROC-AUC was the primary metric, with comparisons to baselines like test-time augmentation. Ablation studies analyzed the impact of referee configurations.
Results
SegFormer-B0 achieved the highest ROC-AUC (0.960), outperforming the same-architecture referee (0.923) and test-time augmentation (+0.055). On subsets excluding empty masks, it maintained superior performance with ROC-AUC of 0.876.
Applications
RBQE can be directly applied in real-time colonoscopy for reliable polyp segmentation. Its reference-free nature also makes it suitable for other medical imaging tasks without ground-truth annotations.
Limitations & Outlook
RBQE's performance depends on referee model selection, which may require task-specific tuning. It has not been validated on larger, more diverse datasets, and handling of empty masks may oversimplify certain cases.
Plain Language Accessible to non-experts
Imagine a teacher grading essays. To ensure fairness, the teacher asks another teacher to grade the same essays. If both teachers give similar grades, the essays are likely graded accurately. If their grades differ significantly, it might indicate a problem. RBQE works similarly by comparing the outputs of two independently trained models to assess the reliability of segmentation results.
ELI14 Explained like you're 14
Imagine you're drawing a map for a treasure hunt. After you're done, you ask a friend to draw the same map. If your maps match, you probably got it right! If they don't, one of you might have made a mistake. RBQE does the same thing but with computer models drawing maps of medical images. Cool, right?
Glossary
RBQE (Referee-Based Quality Estimation)
A method to estimate segmentation reliability by comparing outputs of independently trained models.
Used for reference-free quality estimation in polyp segmentation.
Agreement Dice
A metric measuring overlap between two segmentation masks. Higher values indicate stronger agreement.
Quantifies consistency between primary and referee models.
SegFormer-B0
A lightweight transformer-based semantic segmentation model.
Used as a cross-architecture referee in RBQE experiments.
MedSAM
A prompt-driven medical segmentation model based on the Segment Anything framework.
Evaluated as a prompt-coupled referee in RBQE.
Test-Time Augmentation
A method generating multiple predictions via input transformations.
Used as a baseline for comparison with RBQE.
Open Questions Unanswered questions from this research
- 1 How does RBQE perform on larger, more diverse datasets?
- 2 Can referee model designs be optimized for better reliability signals?
Applications
Immediate Applications
Real-time colonoscopy
Provides reliability estimation for automated polyp segmentation, aiding clinical decision-making.
Medical image segmentation
Applicable to reference-free segmentation tasks like tumor or organ detection.
Long-term Vision
General medical image QA
Develop a universal framework for quality assessment across diverse medical imaging tasks.
Abstract
In real-time colonoscopy, ground-truth annotations are unavailable at inference, so polyp segmentation models can fail silently. We propose Referee-Based Quality Estimation (RBQE), a reference-free framework measuring agreement between a primary segmentation model and an independently trained referee on the same image. RBQE is evaluated on a standardized 1,223-image external benchmark drawn from four public datasets, using four referee configurations chosen to separate two design axes: referee independence and architectural diversity. Using a common Agreement Dice descriptor, a same-architecture referee differing from the primary model only in random initialization already yields a useful reliability signal (ROC-AUC = 0.923), showing that independent training alone is sufficient. Cross-architecture referees improve further: SegFormer-B0 achieves the strongest performance (ROC-AUC = 0.960), significantly outperforming the same-architecture control and UNet++, and exceeding a representative Test-Time Augmentation baseline by 0.055 ROC-AUC under an identical protocol, whereas a prompt-coupled MedSAM referee underperforms despite maximal architectural diversity. Because empty-mask agreement is trivially separable, we also report a restricted evaluation excluding such cases: ROC-AUC falls to 0.876 (SegFormer-B0, 1,046 images) and 0.783 (same-architecture control, 975 images), yet RBQE's margin over both baselines widens on this identical subset. RBQE additionally increases the mean Dice of retained predictions as low-agreement cases are progressively rejected, supporting selective prediction, and requires only one additional deterministic referee forward pass at inference. Our study therefore supports cross-model agreement as a practical, interpretable reliability framework for automated polyp segmentation.