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New CSG-Mamba model enhances endoscopic polyp segmentation accuracy

By PulseAugur Editorial · [1 sources] ·

Researchers have developed CSG-Mamba, a new convolutional scoring gating Vision State Space network designed for improved endoscopic polyp segmentation. This model, built on a VM-UNet architecture, incorporates a Convolutional Scoring Gating module at its bottleneck to recalibrate state-space features. Experiments on the Kvasir-SEG and CVC-ColonDB datasets demonstrate that CSG-Mamba outperforms baseline models in overlap and recall metrics while maintaining competitive boundary accuracy. AI

IMPACT This model could improve the accuracy of computer-aided colonoscopy, potentially leading to earlier and more precise polyp detection.

RANK_REASON The cluster contains a research paper detailing a novel model for a specific computer vision task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New CSG-Mamba model enhances endoscopic polyp segmentation accuracy

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The cluster contains a research paper detailing a novel model for a specific computer vision task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Yuliang Wang, Jiaqi Wu, Jiaye Song, Shuxia Ren ·

    CSG-Mamba: A Convolutional Scoring Gating Vision State Space Network for Endoscopic Polyp Segmentation

    arXiv:2608.14146v1 Announce Type: new Abstract: 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 shifts. Vision S…

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