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MPCGNet: A Multiscale Feature Extraction and Progressive Feature Aggregation Network Using Coupling Gates for Polyp Segmentation

arXiv 2511.11032 ↗First posted 14 Nov 2025cs.CVcited by 0

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Abstract (arXiv)

Automatic segmentation methods of polyps is crucial for assisting doctors in colorectal polyp screening and cancer diagnosis. Despite the progress made by existing methods, polyp segmentation faces several challenges: (1) small-sized polyps are prone to being missed during identification, (2) the boundaries between polyps and the surrounding environment are often ambiguous, (3) noise in colonoscopy images, caused by uneven lighting and other factors, affects segmentation results. To address these challenges, this paper introduces coupling gates as components in specific modules to filter noise and perform feature importance selection. Three modules are proposed: the coupling gates multiscale feature extraction (CGMFE) module, which effectively extracts local features and suppresses noise; the windows cross attention (WCAD) decoder module, which restores details after capturing the precise location of polyps; and the decoder feature aggregation (DFA) module, which progressively aggregates features, further extracts them, and performs feature importance selection to reduce the loss of small-sized polyps. Experimental results demonstrate that MPCGNet outperforms recent networks, with mDice scores 2.20% and 0.68% higher than the second-best network on the ETIS-LaribPolypDB and CVC-ColonDB datasets, respectively.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 19 of its 34 references that are in the map. First posted on arXiv 14 Nov 2025.

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19 of 34 references.

References · 19

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  11. 2019Kvasir-SEG: A Segmented Polyp Datasetcited by 588
  12. 2017Structure-measure: A New Way to Evaluate Foreground Mapscited by 480
7 more
  1. 2020PraNet: Parallel Reverse Attention Network for Polyp Segmentationcited by 460
  2. 2018Enhanced-alignment Measure for Binary Foreground Map Evaluationcited by 370
  3. 2016A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Imagescited by 220
  4. 2021Polarized Self-Attention: Towards High-quality Pixel-wise Regressioncited by 39
  5. 2023Rethinking Mobile Block for Efficient Attention-based Modelscited by 36
  6. 2023MEGANet: Multi-Scale Edge-Guided Attention Network for Weak Boundary Polyp Segmentationcited by 28
  7. 2023M3FPolypSegNet: Segmentation Network with Multi-frequency Feature Fusion for Polyp Localization in Colonoscopy Imagescited by 3

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