Synergistic Multi-Granularity Rough Attention UNet for Polyp Segmentation

TL;DR

This paper introduces a new deep learning model for accurately segmenting polyps in colonoscopic images, improving detection and treatment of colorectal cancer.

Contribution

The paper proposes S-MGRAUNet, a novel architecture with multi-granularity modules for enhanced polyp segmentation.

Findings

S-MGRAUNet outperforms existing methods on ColonDB and CVC-300 datasets.

The model achieves robust and generalized performance on Kvasir-SEG and ClinicDB.

It reduces computational complexity while maintaining high segmentation accuracy.

Abstract

Automatic polyp segmentation in colonoscopic images is crucial for the early detection and treatment of colorectal cancer. However, complex backgrounds, diverse polyp morphologies, and ambiguous boundaries make this task difficult. To address these issues, we propose the Synergistic Multi-Granularity Rough Attention U-Net (S-MGRAUNet), which integrates three key modules: the Multi-Granularity Hybrid Filtering (MGHF) module for extracting multi-scale contextual information, the Dynamic Granularity Partition Synergy (DGPS) module for enhancing polyp-background differentiation through adaptive feature interaction, and the Multi-Granularity Rough Attention (MGRA) mechanism for further optimizing boundary recognition. Extensive experiments on the ColonDB and CVC-300 datasets demonstrate that S-MGRAUNet significantly outperforms existing methods while achieving competitive results on the Kvasir-SEG and ClinicDB datasets, validating its segmentation accuracy, robustness, and generalization capability, all while effectively reducing computational complexity. This study highlights the value of multi-granularity feature extraction and attention mechanisms, providing new insights and practical guidance for advancing multi-granularity theories in medical image segmentation.

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