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<!DOCTYPE html> <html lang="en" class="no-js"> <head> <meta charset="UTF-8"> <meta http-equiv="X-UA-Compatible" content="IE=edge"> <meta name="applicable-device" content="pc,mobile"> <meta name="viewport" content="width=device-width, initial-scale=1"> <meta name="robots" content="max-image-preview:large"> <meta name="access" content="No"> <meta name="360-site-verification" content="1268d79b5e96aecf3ff2a7dac04ad990" /> <title>SMRU-Net: skin disease image segmentation using channel-space separate attention with depthwise separable convolutions | Pattern Analysis and Applications</title> <meta name="twitter:site" content="@SpringerLink"/> <meta name="twitter:card" content="summary_large_image"/> <meta name="twitter:image:alt" content="Content cover image"/> <meta name="twitter:title" content="SMRU-Net: skin disease image segmentation using channel-space separate attention with depthwise separable convolutions"/> <meta name="twitter:description" content="Pattern Analysis and Applications - Skin disease image segmentation faces two major challenges: the complex and varied lesion morphology and the presence of interfering image backgrounds. To..."/> <meta name="twitter:image" content="https://static-content.springer.com/image/art%3A10.1007%2Fs10044-024-01307-7/MediaObjects/10044_2024_1307_Fig1_HTML.png"/> <meta name="journal_id" content="10044"/> <meta name="dc.title" content="SMRU-Net: skin disease image segmentation using channel-space separate attention with depthwise separable convolutions"/> <meta name="dc.source" content="Pattern Analysis and Applications 2024 27:3"/> <meta name="dc.format" content="text/html"/> <meta name="dc.publisher" content="Springer"/> <meta name="dc.date" content="2024-08-03"/> <meta name="dc.type" content="OriginalPaper"/> <meta name="dc.language" content="En"/> <meta name="dc.copyright" content="2024 The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature"/> <meta name="dc.rights" content="2024 The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature"/> <meta name="dc.rightsAgent" content="journalpermissions@springernature.com"/> <meta name="dc.description" content="Skin disease image segmentation faces two major challenges: the complex and varied lesion morphology and the presence of interfering image backgrounds. To address these difficulties in skin disease image segmentation, we propose a Residual U-Net architecture with Channel-Space Separate Attention based on depthwise separable convolutions. The multi-scale residual U-Net modules in the encoder efficiently capture multi-scale texture information in lesions and backgrounds within a single stage, overcoming the limitations of U-Net in extracting just local features. The introduction of ConvMixer Block for global contextual modeling contributes to suppress complex background interference and enhances the overall understanding of lesion morphology. Additionally, we employ a Channel-Space Separate Attention mechanism with depthwise separable convolutions(CSSA-DSC) for feature fusion, effectively addressing the limited expressiveness issue associated with U-Net’s direct skip-connection concatenation. Experimental results on the PH2, ISIC 2017, and ISIC 2018 datasets demonstrate our method’s strong multi-scale modeling and feature expression capabilities."/> <meta name="prism.issn" content="1433-755X"/> <meta name="prism.publicationName" content="Pattern Analysis and Applications"/> <meta name="prism.publicationDate" content="2024-08-03"/> <meta name="prism.volume" content="27"/> <meta name="prism.number" content="3"/> <meta name="prism.section" content="OriginalPaper"/> <meta name="prism.startingPage" content="1"/> <meta name="prism.endingPage" content="14"/> <meta name="prism.copyright" content="2024 The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature"/> <meta name="prism.rightsAgent" content="journalpermissions@springernature.com"/> <meta name="prism.url" content="https://link.springer.com/article/10.1007/s10044-024-01307-7"/> <meta name="prism.doi" content="doi:10.1007/s10044-024-01307-7"/> <meta name="citation_pdf_url" content="https://link.springer.com/content/pdf/10.1007/s10044-024-01307-7.pdf"/> <meta name="citation_fulltext_html_url" content="https://link.springer.com/article/10.1007/s10044-024-01307-7"/> <meta name="citation_journal_title" content="Pattern Analysis and Applications"/> <meta name="citation_journal_abbrev" content="Pattern Anal Applic"/> <meta name="citation_publisher" content="Springer London"/> <meta name="citation_issn" content="1433-755X"/> <meta name="citation_title" content="SMRU-Net: skin disease image segmentation using channel-space separate attention with depthwise separable convolutions"/> <meta name="citation_volume" content="27"/> <meta name="citation_issue" content="3"/> <meta name="citation_publication_date" content="2024/09"/> <meta name="citation_online_date" content="2024/08/03"/> <meta name="citation_firstpage" content="1"/> <meta name="citation_lastpage" content="14"/> <meta name="citation_article_type" content="Original Article"/> <meta name="citation_language" content="en"/> <meta name="dc.identifier" content="doi:10.1007/s10044-024-01307-7"/> <meta name="DOI" content="10.1007/s10044-024-01307-7"/> <meta name="size" content="313947"/> <meta name="citation_doi" content="10.1007/s10044-024-01307-7"/> <meta name="citation_springer_api_url" content="http://api.springer.com/xmldata/jats?q=doi:10.1007/s10044-024-01307-7&api_key="/> <meta name="description" content="Skin disease image segmentation faces two major challenges: the complex and varied lesion morphology and the presence of interfering image backgrounds. 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To address these difficulties in skin disease image segmentation, we propose a Residual U-Net architecture with Channel-Space Separate Attention based on depthwise separable convolutions. The multi-scale residual U-Net modules in the encoder efficiently capture multi-scale texture information in lesions and backgrounds within a single stage, overcoming the limitations of U-Net in extracting just local features. The introduction of ConvMixer Block for global contextual modeling contributes to suppress complex background interference and enhances the overall understanding of lesion morphology. Additionally, we employ a Channel-Space Separate Attention mechanism with depthwise separable convolutions(CSSA-DSC) for feature fusion, effectively addressing the limited expressiveness issue associated with U-Net’s direct skip-connection concatenation. Experimental results on the PH2, ISIC 2017, and ISIC 2018 datasets demonstrate our method’s strong multi-scale modeling and feature expression capabilities."/> <meta property="og:image" content="https://static-content.springer.com/image/art%3A10.1007%2Fs10044-024-01307-7/MediaObjects/10044_2024_1307_Fig1_HTML.png"/> <meta name="format-detection" content="telephone=no"> <link rel="apple-touch-icon" sizes="180x180" href=/oscar-static/img/favicons/darwin/apple-touch-icon-92e819bf8a.png> <link rel="icon" type="image/png" sizes="192x192" href=/oscar-static/img/favicons/darwin/android-chrome-192x192-6f081ca7e5.png> <link rel="icon" type="image/png" sizes="32x32" href=/oscar-static/img/favicons/darwin/favicon-32x32-1435da3e82.png> <link rel="icon" type="image/png" sizes="16x16" href=/oscar-static/img/favicons/darwin/favicon-16x16-ed57f42bd2.png> <link rel="shortcut icon" data-test="shortcut-icon" href=/oscar-static/img/favicons/darwin/favicon-c6d59aafac.ico> <meta name="theme-color" 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To address these difficulties in skin disease image segmentation, we propose a Residual U-Net architecture with Channel-Space Separate Attention based on depthwise separable convolutions. The multi-scale residual U-Net modules in the encoder efficiently capture multi-scale texture information in lesions and backgrounds within a single stage, overcoming the limitations of U-Net in extracting just local features. The introduction of ConvMixer Block for global contextual modeling contributes to suppress complex background interference and enhances the overall understanding of lesion morphology. Additionally, we employ a Channel-Space Separate Attention mechanism with depthwise separable convolutions(CSSA-DSC) for feature fusion, effectively addressing the limited expressiveness issue associated with U-Net’s direct skip-connection concatenation. 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id="Abs1">Abstract</h2><div class="c-article-section__content" id="Abs1-content"><p>Skin disease image segmentation faces two major challenges: the complex and varied lesion morphology and the presence of interfering image backgrounds. To address these difficulties in skin disease image segmentation, we propose a Residual U-Net architecture with Channel-Space Separate Attention based on depthwise separable convolutions. The multi-scale residual U-Net modules in the encoder efficiently capture multi-scale texture information in lesions and backgrounds within a single stage, overcoming the limitations of U-Net in extracting just local features. The introduction of ConvMixer Block for global contextual modeling contributes to suppress complex background interference and enhances the overall understanding of lesion morphology. Additionally, we employ a Channel-Space Separate Attention mechanism with depthwise separable convolutions(CSSA-DSC) for feature fusion, effectively addressing the limited expressiveness issue associated with U-Net’s direct skip-connection concatenation. Experimental results on the PH2, ISIC 2017, and ISIC 2018 datasets demonstrate our method’s strong multi-scale modeling and feature expression capabilities.</p></div></div></section> <div class="c-notes"> <p class="c-notes__text c-status-message--info"> <svg width="24" height="24" focusable="false" role="img" aria-hidden="true" class="c-status-message__icon"> <use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-info-filled-medium"></use> </svg> This is a preview of subscription content, <a id="test-login-banner-link" href="//wayf.springernature.com?redirect_uri=https%3A%2F%2Flink.springer.com%2Farticle%2F10.1007%2Fs10044-024-01307-7%3Ferror%3Dcookies_not_supported%26code%3Dc10b20ae-2207-419b-b07e-32dfda1a2014" data-track="click" data-track-action="login" data-track-label="link" class="c-preview-message__link">log in via an institution</a> <svg width="16" height="16" focusable="false" role="img" aria-hidden="true" class="u-icon c-external-link__icon"> <use 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id="citeas">Cite this article</h3><p class="c-bibliographic-information__citation">Liu, S., Wang, P., Lin, Y. <i>et al.</i> SMRU-Net: skin disease image segmentation using channel-space separate attention with depthwise separable convolutions. <i>Pattern Anal Applic</i> <b>27</b>, 93 (2024). https://doi.org/10.1007/s10044-024-01307-7</p><p class="c-bibliographic-information__download-citation u-hide-print"><a data-test="citation-link" data-track="click" data-track-action="download article citation" data-track-label="link" data-track-external="" rel="nofollow" href="https://citation-needed.springer.com/v2/references/10.1007/s10044-024-01307-7?format=refman&flavour=citation">Download citation<svg width="16" height="16" focusable="false" role="img" aria-hidden="true" class="u-icon"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-download-medium"></use></svg></a></p><ul class="c-bibliographic-information__list" data-test="publication-history"><li class="c-bibliographic-information__list-item"><p>Received<span class="u-hide">: </span><span class="c-bibliographic-information__value"><time datetime="2024-04-30">30 April 2024</time></span></p></li><li class="c-bibliographic-information__list-item"><p>Accepted<span class="u-hide">: </span><span class="c-bibliographic-information__value"><time datetime="2024-07-23">23 July 2024</time></span></p></li><li class="c-bibliographic-information__list-item"><p>Published<span class="u-hide">: </span><span class="c-bibliographic-information__value"><time datetime="2024-08-03">03 August 2024</time></span></p></li><li class="c-bibliographic-information__list-item c-bibliographic-information__list-item--full-width"><p><abbr title="Digital Object Identifier">DOI</abbr><span class="u-hide">: </span><span class="c-bibliographic-information__value">https://doi.org/10.1007/s10044-024-01307-7</span></p></li></ul><div data-component="share-box"><div class="c-article-share-box u-display-none" 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