[2011.11750] Federated Semi-Supervised Learning for COVID Region Segmentation in Chest CT using Multi-National Data from China, Italy, Japan

<?xml version="1.0" encoding="UTF-8"?> <!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> <html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"> <head> <title>[2011.11750] Federated Semi-Supervised Learning for COVID Region Segmentation in Chest CT using Multi-National Data from China, Italy, Japan</title> <meta name="viewport" content="width=device-width, initial-scale=1"> <link rel="apple-touch-icon" sizes="180x180" href="/static/browse/0.3.4/images/icons/apple-touch-icon.png"> <link rel="icon" type="image/png" sizes="32x32" href="/static/browse/0.3.4/images/icons/favicon-32x32.png"> <link rel="icon" type="image/png" sizes="16x16" href="/static/browse/0.3.4/images/icons/favicon-16x16.png"> <link rel="manifest" href="/static/browse/0.3.4/images/icons/site.webmanifest"> <link rel="mask-icon" href="/static/browse/0.3.4/images/icons/safari-pinned-tab.svg" color="#5bbad5"> <meta name="msapplication-TileColor" content="#da532c"> <meta name="theme-color" content="#ffffff"> <link rel="stylesheet" type="text/css" media="screen" href="/static/browse/0.3.4/css/arXiv.css?v=20240822" /> <link rel="stylesheet" type="text/css" media="print" href="/static/browse/0.3.4/css/arXiv-print.css?v=20200611" /> <link rel="stylesheet" type="text/css" media="screen" href="/static/browse/0.3.4/css/browse_search.css" /> <script language="javascript" src="/static/browse/0.3.4/js/accordion.js" /></script> <link rel="canonical" href="https://arxiv.org/abs/2011.11750"/> <meta name="description" content="Abstract page for arXiv paper 2011.11750: Federated Semi-Supervised Learning for COVID Region Segmentation in Chest CT using Multi-National Data from China, Italy, Japan"><meta property="og:type" content="website" /> <meta property="og:site_name" content="arXiv.org" /> <meta property="og:title" content="Federated Semi-Supervised Learning for COVID Region Segmentation in Chest CT using Multi-National Data from China, Italy, Japan" /> <meta property="og:url" content="https://arxiv.org/abs/2011.11750v1" /> <meta property="og:image" content="/static/browse/0.3.4/images/arxiv-logo-fb.png" /> <meta property="og:image:secure_url" content="/static/browse/0.3.4/images/arxiv-logo-fb.png" /> <meta property="og:image:width" content="1200" /> <meta property="og:image:height" content="700" /> <meta property="og:image:alt" content="arXiv logo"/> <meta property="og:description" content="The recent outbreak of COVID-19 has led to urgent needs for reliable diagnosis and management of SARS-CoV-2 infection. As a complimentary tool, chest CT has been shown to be able to reveal visual patterns characteristic for COVID-19, which has definite value at several stages during the disease course. To facilitate CT analysis, recent efforts have focused on computer-aided characterization and diagnosis, which has shown promising results. However, domain shift of data across clinical data centers poses a serious challenge when deploying learning-based models. In this work, we attempt to find a solution for this challenge via federated and semi-supervised learning. A multi-national database consisting of 1704 scans from three countries is adopted to study the performance gap, when training a model with one dataset and applying it to another. Expert radiologists manually delineated 945 scans for COVID-19 findings. In handling the variability in both the data and annotations, a novel federated semi-supervised learning technique is proposed to fully utilize all available data (with or without annotations). Federated learning avoids the need for sensitive data-sharing, which makes it favorable for institutions and nations with strict regulatory policy on data privacy. Moreover, semi-supervision potentially reduces the annotation burden under a distributed setting. The proposed framework is shown to be effective compared to fully supervised scenarios with conventional data sharing instead of model weight sharing."/> <meta name="twitter:site" content="@arxiv"/> <meta name="twitter:card" content="summary"/> <meta name="twitter:title" content="Federated Semi-Supervised Learning for COVID Region Segmentation..."/> <meta name="twitter:description" content="The recent outbreak of COVID-19 has led to urgent needs for reliable diagnosis and management of SARS-CoV-2 infection. As a complimentary tool, chest CT has been shown to be able to reveal visual..."/> <meta name="twitter:image" content="https://static.arxiv.org/icons/twitter/arxiv-logo-twitter-square.png"/> <meta name="twitter:image:alt" content="arXiv logo"/> <link rel="stylesheet" media="screen" type="text/css" href="/static/browse/0.3.4/css/tooltip.css"/><link rel="stylesheet" media="screen" type="text/css" href="https://static.arxiv.org/js/bibex-dev/bibex.css?20200709"/> <script src="/static/browse/0.3.4/js/mathjaxToggle.min.js" type="text/javascript"></script> <script src="//code.jquery.com/jquery-latest.min.js" type="text/javascript"></script> <script src="//cdn.jsdelivr.net/npm/js-cookie@2/src/js.cookie.min.js" type="text/javascript"></script> <script src="//cdn.jsdelivr.net/npm/dompurify@2.3.5/dist/purify.min.js"></script> <script src="/static/browse/0.3.4/js/toggle-labs.js?20241022" type="text/javascript"></script> <script src="/static/browse/0.3.4/js/cite.js" type="text/javascript"></script><meta name="citation_title" content="Federated Semi-Supervised Learning for COVID Region Segmentation in Chest CT using Multi-National Data from China, Italy, Japan" /><meta name="citation_author" content="Yang, Dong" /><meta name="citation_author" content="Xu, Ziyue" /><meta name="citation_author" content="Li, Wenqi" /><meta name="citation_author" content="Myronenko, Andriy" /><meta name="citation_author" content="Roth, Holger R." /><meta name="citation_author" content="Harmon, Stephanie" /><meta name="citation_author" content="Xu, Sheng" /><meta name="citation_author" content="Turkbey, Baris" /><meta name="citation_author" content="Turkbey, Evrim" /><meta name="citation_author" content="Wang, Xiaosong" /><meta name="citation_author" content="Zhu, Wentao" /><meta name="citation_author" content="Carrafiello, Gianpaolo" /><meta name="citation_author" content="Patella, Francesca" /><meta name="citation_author" content="Cariati, Maurizio" /><meta name="citation_author" content="Obinata, Hirofumi" /><meta name="citation_author" content="Mori, Hitoshi" /><meta name="citation_author" content="Tamura, Kaku" /><meta name="citation_author" content="An, Peng" /><meta name="citation_author" content="Wood, Bradford J." /><meta name="citation_author" content="Xu, Daguang" /><meta name="citation_date" content="2020/11/23" /><meta name="citation_online_date" content="2020/11/23" /><meta name="citation_pdf_url" content="http://arxiv.org/pdf/2011.11750" /><meta name="citation_arxiv_id" content="2011.11750" /><meta name="citation_abstract" content="The recent outbreak of COVID-19 has led to urgent needs for reliable diagnosis and management of SARS-CoV-2 infection. As a complimentary tool, chest CT has been shown to be able to reveal visual patterns characteristic for COVID-19, which has definite value at several stages during the disease course. To facilitate CT analysis, recent efforts have focused on computer-aided characterization and diagnosis, which has shown promising results. However, domain shift of data across clinical data centers poses a serious challenge when deploying learning-based models. In this work, we attempt to find a solution for this challenge via federated and semi-supervised learning. A multi-national database consisting of 1704 scans from three countries is adopted to study the performance gap, when training a model with one dataset and applying it to another. Expert radiologists manually delineated 945 scans for COVID-19 findings. In handling the variability in both the data and annotations, a novel federated semi-supervised learning technique is proposed to fully utilize all available data (with or without annotations). Federated learning avoids the need for sensitive data-sharing, which makes it favorable for institutions and nations with strict regulatory policy on data privacy. Moreover, semi-supervision potentially reduces the annotation burden under a distributed setting. The proposed framework is shown to be effective compared to fully supervised scenarios with conventional data sharing instead of model weight sharing." /> </head> <body class="with-cu-identity"> <div class="flex-wrap-footer"> <header> <a href="#content" class="is-sr-only">Skip to main content</a>  <div class="columns is-vcentered is-hidden-mobile" id="cu-identity"> <div class="column" id="cu-logo"> <a href="https://www.cornell.edu/"><img src="/static/browse/0.3.4/images/icons/cu/cornell-reduced-white-SMALL.svg" alt="Cornell University" /></a> </div><div class="column" id="support-ack"> <span id="support-ack-url">We gratefully acknowledge support from the Simons Foundation, <a href="https://info.arxiv.org/about/ourmembers.html">member institutions</a>, and all contributors.</span> <a href="https://info.arxiv.org/about/donate.html" class="btn-header-donate">Donate</a> </div> </div> <div id="header" class="is-hidden-mobile"> <a aria-hidden="true" href="{url_path('ignore_me')}"></a> <div 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rel="stylesheet" type="text/css" href="/static/base/1.0.1/css/abs.css"> <div id="content-inner"> <div id="abs"> <div class="dateline"> [Submitted on 23 Nov 2020]</div> <h1 class="title mathjax"><span class="descriptor">Title:</span>Federated Semi-Supervised Learning for COVID Region Segmentation in Chest CT using Multi-National Data from China, Italy, Japan</h1> <div class="authors"><span class="descriptor">Authors:</span><a href="https://arxiv.org/search/eess?searchtype=author&query=Yang,+D" rel="nofollow">Dong Yang</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Xu,+Z" rel="nofollow">Ziyue Xu</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Li,+W" rel="nofollow">Wenqi Li</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Myronenko,+A" rel="nofollow">Andriy Myronenko</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Roth,+H+R" rel="nofollow">Holger R. Roth</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Harmon,+S" rel="nofollow">Stephanie Harmon</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Xu,+S" rel="nofollow">Sheng Xu</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Turkbey,+B" rel="nofollow">Baris Turkbey</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Turkbey,+E" rel="nofollow">Evrim Turkbey</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Wang,+X" rel="nofollow">Xiaosong Wang</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Zhu,+W" rel="nofollow">Wentao Zhu</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Carrafiello,+G" rel="nofollow">Gianpaolo Carrafiello</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Patella,+F" rel="nofollow">Francesca Patella</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Cariati,+M" rel="nofollow">Maurizio Cariati</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Obinata,+H" rel="nofollow">Hirofumi Obinata</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Mori,+H" rel="nofollow">Hitoshi Mori</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Tamura,+K" rel="nofollow">Kaku Tamura</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=An,+P" rel="nofollow">Peng An</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Wood,+B+J" rel="nofollow">Bradford J. Wood</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Xu,+D" rel="nofollow">Daguang Xu</a></div> <div id="download-button-info" hidden>View a PDF of the paper titled Federated Semi-Supervised Learning for COVID Region Segmentation in Chest CT using Multi-National Data from China, Italy, Japan, by Dong Yang and 19 other authors</div> <a class="mobile-submission-download" href="/pdf/2011.11750">View PDF</a> <blockquote class="abstract mathjax"> <span class="descriptor">Abstract:</span>The recent outbreak of COVID-19 has led to urgent needs for reliable diagnosis and management of SARS-CoV-2 infection. As a complimentary tool, chest CT has been shown to be able to reveal visual patterns characteristic for COVID-19, which has definite value at several stages during the disease course. To facilitate CT analysis, recent efforts have focused on computer-aided characterization and diagnosis, which has shown promising results. However, domain shift of data across clinical data centers poses a serious challenge when deploying learning-based models. In this work, we attempt to find a solution for this challenge via federated and semi-supervised learning. A multi-national database consisting of 1704 scans from three countries is adopted to study the performance gap, when training a model with one dataset and applying it to another. Expert radiologists manually delineated 945 scans for COVID-19 findings. In handling the variability in both the data and annotations, a novel federated semi-supervised learning technique is proposed to fully utilize all available data (with or without annotations). Federated learning avoids the need for sensitive data-sharing, which makes it favorable for institutions and nations with strict regulatory policy on data privacy. Moreover, semi-supervision potentially reduces the annotation burden under a distributed setting. The proposed framework is shown to be effective compared to fully supervised scenarios with conventional data sharing instead of model weight sharing. </blockquote>  <div class="metatable"> <table summary="Additional metadata"> <tr> <td class="tablecell label">Comments:</td> <td class="tablecell comments mathjax">Accepted with minor revision to Medical Image Analysis</td> </tr> <tr> <td class="tablecell label">Subjects:</td> <td class="tablecell subjects"> <span class="primary-subject">Image and Video Processing (eess.IV)</span>; Computer Vision and Pattern Recognition (cs.CV)</td> </tr><tr> <td class="tablecell label">Cite as:</td> <td class="tablecell arxivid"><span class="arxivid"><a href="https://arxiv.org/abs/2011.11750">arXiv:2011.11750</a> [eess.IV]</span></td> </tr> <tr> <td class="tablecell label"> </td> <td class="tablecell arxividv">(or <span class="arxivid"> <a href="https://arxiv.org/abs/2011.11750v1">arXiv:2011.11750v1</a> [eess.IV]</span> for this version) </td> </tr> <tr> <td class="tablecell label"> </td> <td class="tablecell arxivdoi"> <a href="https://doi.org/10.48550/arXiv.2011.11750" id="arxiv-doi-link">https://doi.org/10.48550/arXiv.2011.11750</a><div class="button-and-tooltip"> <button class="more-info" aria-describedby="more-info-desc-1"> <svg height="15" role="presentation" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512"><path fill="currentColor" d="M256 8C119.043 8 8 119.083 8 256c0 136.997 111.043 248 248 248s248-111.003 248-248C504 119.083 392.957 8 256 8zm0 110c23.196 0 42 18.804 42 42s-18.804 42-42 42-42-18.804-42-42 18.804-42 42-42zm56 254c0 6.627-5.373 12-12 12h-88c-6.627 0-12-5.373-12-12v-24c0-6.627 5.373-12 12-12h12v-64h-12c-6.627 0-12-5.373-12-12v-24c0-6.627 5.373-12 12-12h64c6.627 0 12 5.373 12 12v100h12c6.627 0 12 5.373 12 12v24z" class=""></path></svg> <span class="visually-hidden">Focus to learn more</span> </button>  <div role="tooltip" id="more-info-desc-1"> <span class="left-corner"></span> arXiv-issued DOI via 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[2011.11750] Federated Semi-Supervised Learning for COVID Region Segmentation in Chest CT using Multi-National Data from China, Italy, Japan