Polyp Segmentation
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Benchmarks
Kvasir-SEG
PolypGen
Most implemented
ResUNet++: An Advanced Architecture for Medical Image Segmentation
Metrics or Mirage? An Audit of Evaluation Inconsistencies in Colonoscopy Polyp Segmentation Benchmarks
KDAS: Knowledge Distillation via Attention Supervision Framework for Polyp Segmentation
TransNetR: Transformer-based Residual Network for Polyp Segmentation with Multi-Center Out-of-Distribution Testing
Multi Kernel Positional Embedding ConvNeXt for Polyp Segmentation
Papers
Cross-Model Agreement as a Deployment-Time Reliability Signal for Automatic Polyp Segmentation
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 ag…
Polyp SegmentationCSG-Mamba: A Convolutional Scoring Gating Vision State Space Network for Endoscopic Polyp Segmentation
Accurate polyp segmentation is critical for computer-aided colonoscopy, yet endoscopic images often contain low-contrast boundaries, mucosal texture interference, specular highlights, and device-dependent appearance shif…
Long-range modelingPolyp SegmentationMetrics or Mirage? An Audit of Evaluation Inconsistencies in Colonoscopy Polyp Segmentation Benchmarks
Progress in colonoscopy polyp segmentation is routinely reported through leaderboard comparisons on a small set of public benchmarks. We argue that this apparent progress is difficult to verify: a systematic audit of \te…
Polyp SegmentationRIGS-Refiner: Risk-Guided Recursive Refinement in Prediction Space for Colonoscopy Polyp Segmentation
Post-refinement can improve colonoscopy segmentation after host inference, but many designs still rely on extra correction heads or multi-stage pipelines with non-negligible parameter or computational cost. For polyp seg…
Polyp SegmentationLightweight Polyp Segmentation via a Gain-Aware Prediction-Space Recursive Controller
While lightweight polyp segmentation is highly desirable for low-cost deployment, reported performance gains often stem from upgraded backbone encoders, complex decoders, or heavy refinement branches. Consequently, it re…
Polyp SegmentationGEN-Guard: Correcting Generalization Failures for Deployable Federated Surgical AI
Federated Learning (FL) in surgical video AI enables collaborative model training without sharing sensitive data. However, standard evaluation practices - selecting the "best" global model based only on validation data f…
Surgical phase recognitionZero-shot GeneralizationFederated LearningPolyp Segmentation