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The paper introduces ASGNet, a novel deep learning architecture for polyp segmentation in colonoscopy images, designed to overcome limitations in spatial perception by integrating spectral features with global attributes. ASGNet incorporates a spectrum-guided non-local perception module for aggregating local and global information, a multi-source semantic extractor for polyp localization, and a dense cross-layer interaction decoder for generating high-quality representations. Experiments on five benchmarks demonstrate that ASGNet outperforms 21 state-of-the-art methods, achieving superior polyp segmentation accuracy.
By cleverly fusing spectral and spatial information, ASGNet achieves state-of-the-art polyp segmentation, suggesting that moving beyond purely spatial processing can unlock significant gains in medical image analysis.
Early identification and removal of polyps can reduce the risk of developing colorectal cancer. However, the diverse morphologies, complex backgrounds and often concealed nature of polyps make polyp segmentation in colonoscopy images highly challenging. Despite the promising performance of existing deep learning-based polyp segmentation methods, their perceptual capabilities remain biased toward local regions, mainly because of the strong spatial correlations between neighboring pixels in the spatial domain. This limitation makes it difficult to capture the complete polyp structures, ultimately leading to sub-optimal segmentation results. In this paper, we propose a novel adaptive spectrum guidance network, called ASGNet, which addresses the limitations of spatial perception by integrating spectral features with global attributes. Specifically, we first design a spectrum-guided non-local perception module that jointly aggregates local and global information, therefore enhancing the discriminability of polyp structures, and refining their boundaries. Moreover, we introduce a multi-source semantic extractor that integrates rich high-level semantic information to assist in the preliminary localization of polyps. Furthermore, we construct a dense cross-layer interaction decoder that effectively integrates diverse information from different layers and strengthens it to generate high-quality representations for accurate polyp segmentation. Extensive quantitative and qualitative results demonstrate the superiority of our ASGNet approach over 21 state-of-the-art methods across five widely-used polyp segmentation benchmarks. The code will be publicly available at: https://github.com/CSYSI/ASGNet.
