文献类型：期刊文献

英文题名：ASGNet: Adaptive Spectrum Guidance Network for Automatic Polyp Segmentation

作者：Sun, Yanguang[1,2];Zhang, Hengmin[3,4,5];Qian, Jianjun[1,2];Yang, Jian[1,2];Luo, Lei[1,2]

机构：[1]Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, PCA Lab, Nanjing 210094, Peoples R China;[2]Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Key Lab Intelligent Percept & Syst High Dimens Inf, Minist Educ, Nanjing 210094, Peoples R China;[3]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China;[4]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[5]Shanghai Key Lab Data Sci, Shanghai 200438, Peoples R China

年份：2026

卷号：36

期号：8

起止页码：11876

外文期刊名：IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY

收录：;EI(收录号：20260225095);WOS:【SCI-EXPANDED(收录号:WOS:001841856300003)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 62276135, Grant 61806094, and Grant 62176124; in part by the National Natural Science Fund for Excellent Young Scientists Fund Program (Overseas); and in part by the Open Project Program of Shanghai Key Laboratory of Data Science under Grant 2025090600010.

语种：英文

外文关键词：Feeds; Antennas; Circuits and systems; Filtering; Filters; MIMICs; Millimeter wave integrated circuits; Monolithic integrated circuits; Videos; Video equipment; Colorectal cancer; colonoscopy images; neural networks; polyp segmentation

摘要：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