{"id":31802,"date":"2026-07-08T10:35:31","date_gmt":"2026-07-08T08:35:31","guid":{"rendered":"https:\/\/sano.science\/?p=31802"},"modified":"2026-07-08T10:36:26","modified_gmt":"2026-07-08T08:36:26","slug":"new-sano-publication-on-federated-learning-in-medical-imaging","status":"publish","type":"post","link":"https:\/\/sano.science\/new-sano-publication-on-federated-learning-in-medical-imaging\/","title":{"rendered":"New Sano publication on federated learning in medical imaging"},"content":{"rendered":"\n<p class=\"eplus-wrapper wp-block-paragraph\">We&nbsp;are&nbsp;pleased&nbsp;to&nbsp;share&nbsp;that&nbsp;a&nbsp;new&nbsp;paper&nbsp;titled&nbsp;\u201cFederated learning: A&nbsp;new&nbsp;frontier&nbsp;in the&nbsp;exploration&nbsp;of&nbsp;multi\u2011institutional&nbsp;medical&nbsp;imaging&nbsp;data\u201d,&nbsp;co\u2011authored&nbsp;by <a href=\"https:\/\/sano.science\/people\/dominika-ciupek\/\" data-type=\"people\" data-id=\"12012\">Dominika Ciupek<\/a>, <a href=\"https:\/\/sano.science\/people\/maciej-malawski\/\" data-type=\"people\" data-id=\"531\">Maciej Malawski<\/a> and <a href=\"https:\/\/sano.science\/people\/tomasz-pieciak\/\" data-type=\"people\" data-id=\"13903\">Tomasz&nbsp;Pieciak<\/a>,&nbsp;is&nbsp;now&nbsp;available&nbsp;via&nbsp;ScienceDirect.&nbsp;<\/p>\n\n\n\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n<p class=\"eplus-wrapper wp-block-paragraph\">Modern deep learning methods in medical imaging need very large and diverse datasets to learn robust, clinically useful patterns. At the same time, sharing raw medical images between institutions is difficult&nbsp;due&nbsp;to&nbsp;strict&nbsp;privacy&nbsp;regulations,&nbsp;time\u2011consuming&nbsp;ethical&nbsp;procedures&nbsp;and the&nbsp;need&nbsp;for&nbsp;careful&nbsp;anonymization&nbsp;and data management. The paper focuses on <strong>federated learning (FL)<\/strong> \u2013 an emerging paradigm&nbsp;that&nbsp;allows&nbsp;hospitals&nbsp;to&nbsp;collaborate&nbsp;on AI&nbsp;models&nbsp;without&nbsp;sharing&nbsp;patient&nbsp;data&nbsp;directly.&nbsp;<\/p>\n\n\n\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n<p class=\"eplus-wrapper wp-block-paragraph\">The authors provide the theoretical foundations and a structured overview of federated learning, with a particular focus on medical imaging applications. They review general and specialized aggregation and learning algorithms that make it possible to train a globally generalized model across many institutions while keeping data on\u2011site. The article also highlights key challenges such as data and model heterogeneity, privacy and security risks, and limitations in computation and communication between participating sites, as well as an overview of regulatory frameworks relevant for deploying FL in healthcare.<\/p>\n\n\n\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n<figure class=\"wp-block-image size-full eplus-wrapper\"><img loading=\"lazy\" decoding=\"async\" width=\"659\" height=\"320\" src=\"https:\/\/sano.science\/wp-content\/uploads\/2026\/07\/Federated-learning-A-new-frontier-in-the-exploration-of-multi-institutional-medical-imaging-data.jpg\" alt=\"\" class=\"wp-image-31803\" srcset=\"https:\/\/sano.science\/wp-content\/uploads\/2026\/07\/Federated-learning-A-new-frontier-in-the-exploration-of-multi-institutional-medical-imaging-data.jpg 659w, https:\/\/sano.science\/wp-content\/uploads\/2026\/07\/Federated-learning-A-new-frontier-in-the-exploration-of-multi-institutional-medical-imaging-data-300x146.jpg 300w\" sizes=\"auto, (max-width: 659px) 100vw, 659px\" \/><figcaption class=\"wp-element-caption\"><br>Fig. 1.&nbsp;Comparison between centralized and federated learning approaches:&nbsp;<strong>A.<\/strong>&nbsp;In a centralized architecture, the institutions (here, 1, 2, 3) transfer their local datasets to the central server. Other centers (Institution 4) extract datasets from the global server or use its computing infrastructure to train the DL models.&nbsp;<strong>B.<\/strong>&nbsp;Each institution\u2019s data remains locally preserved in a federated architecture while the parameters of locally trained models&nbsp;are transferred to the central server. The central server aggregates received parameters and sends back the parameters of a global model&nbsp;&nbsp;to each center.<\/figcaption><\/figure>\n\n\n\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n<p class=\"eplus-wrapper wp-block-paragraph\">The&nbsp;full&nbsp;text&nbsp;of \u201cFederated learning: A new frontier in the exploration of multi\u2011institutional medical imaging data\u201d is available here:<\/p>\n\n\n\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n\t\n    \n        \n\t\t\t<a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0169260726002087\" target=\"_blank\" rel= \"noopener noreferrer nofollow\" class=\"button primary \">\n\n\t\t\t\t<span>\n\t\t\t\t\tScienceDirect\u00a0\u2013\u00a0 www.sciencedirect.com\u00a0\n\t\t\t\t<\/span>\n\n\t\t\t<\/a>\n\n        \n    \n","protected":false},"excerpt":"How to train powerful AI models on hospital data without moving sensitive images","author":8,"featured_media":30902,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"editor_plus_post_options":"{}","editor_plus_copied_stylings":"{}","footnotes":""},"categories":[1],"tags":[],"class_list":["post-31802","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v28.0 (Yoast SEO v28.0) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>New Sano publication on federated learning in medical imaging - Centre for Computational Personalized Medicine<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sano.science\/new-sano-publication-on-federated-learning-in-medical-imaging\/\" \/>\n<meta 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class=\" eplus-wrapper\">We&nbsp;are&nbsp;pleased&nbsp;to&nbsp;share&nbsp;that&nbsp;a&nbsp;new&nbsp;paper&nbsp;titled&nbsp;\u201cFederated learning: A&nbsp;new&nbsp;frontier&nbsp;in the&nbsp;exploration&nbsp;of&nbsp;multi\u2011institutional&nbsp;medical&nbsp;imaging&nbsp;data\u201d,&nbsp;co\u2011authored&nbsp;by <a href=\"https:\/\/sano.science\/people\/dominika-ciupek\/\" data-type=\"people\" data-id=\"12012\">Dominika Ciupek<\/a>, <a href=\"https:\/\/sano.science\/people\/maciej-malawski\/\" data-type=\"people\" data-id=\"531\">Maciej Malawski<\/a> and <a href=\"https:\/\/sano.science\/people\/tomasz-pieciak\/\" data-type=\"people\" data-id=\"13903\">Tomasz&nbsp;Pieciak<\/a>,&nbsp;is&nbsp;now&nbsp;available&nbsp;via&nbsp;ScienceDirect.&nbsp;<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">We&nbsp;are&nbsp;pleased&nbsp;to&nbsp;share&nbsp;that&nbsp;a&nbsp;new&nbsp;paper&nbsp;titled&nbsp;\u201cFederated learning: A&nbsp;new&nbsp;frontier&nbsp;in the&nbsp;exploration&nbsp;of&nbsp;multi\u2011institutional&nbsp;medical&nbsp;imaging&nbsp;data\u201d,&nbsp;co\u2011authored&nbsp;by <a href=\"https:\/\/sano.science\/people\/dominika-ciupek\/\" data-type=\"people\" data-id=\"12012\">Dominika Ciupek<\/a>, <a href=\"https:\/\/sano.science\/people\/maciej-malawski\/\" data-type=\"people\" data-id=\"531\">Maciej Malawski<\/a> and <a href=\"https:\/\/sano.science\/people\/tomasz-pieciak\/\" data-type=\"people\" data-id=\"13903\">Tomasz&nbsp;Pieciak<\/a>,&nbsp;is&nbsp;now&nbsp;available&nbsp;via&nbsp;ScienceDirect.&nbsp;<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"10px","epAnimationGeneratedClass":"edplus_anim-PN73jy","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-h5bcCU","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">Modern deep learning methods in medical imaging need very large and diverse datasets to learn robust, clinically useful patterns. At the same time, sharing raw medical images between institutions is difficult&nbsp;due&nbsp;to&nbsp;strict&nbsp;privacy&nbsp;regulations,&nbsp;time\u2011consuming&nbsp;ethical&nbsp;procedures&nbsp;and the&nbsp;need&nbsp;for&nbsp;careful&nbsp;anonymization&nbsp;and data management. The paper focuses on <strong>federated learning (FL)<\/strong> \u2013 an emerging paradigm&nbsp;that&nbsp;allows&nbsp;hospitals&nbsp;to&nbsp;collaborate&nbsp;on AI&nbsp;models&nbsp;without&nbsp;sharing&nbsp;patient&nbsp;data&nbsp;directly.&nbsp;<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">Modern deep learning methods in medical imaging need very large and diverse datasets to learn robust, clinically useful patterns. At the same time, sharing raw medical images between institutions is difficult&nbsp;due&nbsp;to&nbsp;strict&nbsp;privacy&nbsp;regulations,&nbsp;time\u2011consuming&nbsp;ethical&nbsp;procedures&nbsp;and the&nbsp;need&nbsp;for&nbsp;careful&nbsp;anonymization&nbsp;and data management. The paper focuses on <strong>federated learning (FL)<\/strong> \u2013 an emerging paradigm&nbsp;that&nbsp;allows&nbsp;hospitals&nbsp;to&nbsp;collaborate&nbsp;on AI&nbsp;models&nbsp;without&nbsp;sharing&nbsp;patient&nbsp;data&nbsp;directly.&nbsp;<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"10px","epAnimationGeneratedClass":"edplus_anim-9eAepE","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-nvwRLl","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">The authors provide the theoretical foundations and a structured overview of federated learning, with a particular focus on medical imaging applications. They review general and specialized aggregation and learning algorithms that make it possible to train a globally generalized model across many institutions while keeping data on\u2011site. The article also highlights key challenges such as data and model heterogeneity, privacy and security risks, and limitations in computation and communication between participating sites, as well as an overview of regulatory frameworks relevant for deploying FL in healthcare.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">The authors provide the theoretical foundations and a structured overview of federated learning, with a particular focus on medical imaging applications. They review general and specialized aggregation and learning algorithms that make it possible to train a globally generalized model across many institutions while keeping data on\u2011site. The article also highlights key challenges such as data and model heterogeneity, privacy and security risks, and limitations in computation and communication between participating sites, as well as an overview of regulatory frameworks relevant for deploying FL in healthcare.<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"30px","epAnimationGeneratedClass":"edplus_anim-0KwoiX","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/image","attrs":{"id":31803,"sizeSlug":"full","linkDestination":"none","epAnimationGeneratedClass":"edplus_anim-XdMhKE","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<figure class=\"wp-block-image size-full eplus-wrapper\"><img src=\"https:\/\/sano.science\/wp-content\/uploads\/2026\/07\/Federated-learning-A-new-frontier-in-the-exploration-of-multi-institutional-medical-imaging-data.jpg\" alt=\"\" class=\"wp-image-31803\"\/><figcaption class=\"wp-element-caption\"><br>Fig. 1.&nbsp;Comparison between centralized and federated learning approaches:&nbsp;<strong>A.<\/strong>&nbsp;In a centralized architecture, the institutions (here, 1, 2, 3) transfer their local datasets to the central server. Other centers (Institution 4) extract datasets from the global server or use its computing infrastructure to train the DL models.&nbsp;<strong>B.<\/strong>&nbsp;Each institution\u2019s data remains locally preserved in a federated architecture while the parameters of locally trained models&nbsp;are transferred to the central server. The central server aggregates received parameters and sends back the parameters of a global model&nbsp;&nbsp;to each center.<\/figcaption><\/figure>\n","innerContent":["\n<figure class=\"wp-block-image size-full eplus-wrapper\"><img src=\"https:\/\/sano.science\/wp-content\/uploads\/2026\/07\/Federated-learning-A-new-frontier-in-the-exploration-of-multi-institutional-medical-imaging-data.jpg\" alt=\"\" class=\"wp-image-31803\"\/><figcaption class=\"wp-element-caption\"><br>Fig. 1.&nbsp;Comparison between centralized and federated learning approaches:&nbsp;<strong>A.<\/strong>&nbsp;In a centralized architecture, the institutions (here, 1, 2, 3) transfer their local datasets to the central server. Other centers (Institution 4) extract datasets from the global server or use its computing infrastructure to train the DL models.&nbsp;<strong>B.<\/strong>&nbsp;Each institution\u2019s data remains locally preserved in a federated architecture while the parameters of locally trained models&nbsp;are transferred to the central server. The central server aggregates received parameters and sends back the parameters of a global model&nbsp;&nbsp;to each center.<\/figcaption><\/figure>\n"]},{"blockName":"core\/spacer","attrs":{"height":"10px","epAnimationGeneratedClass":"edplus_anim-Atf5XD","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-M4wjDd","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">The&nbsp;full&nbsp;text&nbsp;of \u201cFederated learning: A new frontier in the exploration of multi\u2011institutional medical imaging data\u201d is available here:<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">The&nbsp;full&nbsp;text&nbsp;of \u201cFederated learning: A new frontier in the exploration of multi\u2011institutional medical imaging data\u201d is available here:<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"30px","epAnimationGeneratedClass":"edplus_anim-Atf5XD","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"acf\/button","attrs":{"title":"ScienceDirect\u00a0\u2013\u00a0 www.sciencedirect.com\u00a0","button_type":"link","url":"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0169260726002087","button_style":"primary","target":"_blank","button_extra_classes":""},"innerBlocks":[],"innerHTML":"","innerContent":[]}],"meta_data":{"has_thumbnail_pattern":false,"share_on_social_media":{"has_social_media":false}},"featured_image":{"url":"https:\/\/sano.science\/wp-content\/uploads\/2026\/04\/publications-Sano-1024x544.jpg"},"main_category":{"name":"Uncategorized"},"prev_page":false,"next_page":{"slug":"sano-at-discovery-development-europe-2026-2"},"_links":{"self":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/posts\/31802","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/users\/8"}],"replies":[{"embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/comments?post=31802"}],"version-history":[{"count":16,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/posts\/31802\/revisions"}],"predecessor-version":[{"id":31819,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/posts\/31802\/revisions\/31819"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media\/30902"}],"wp:attachment":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media?parent=31802"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/categories?post=31802"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/tags?post=31802"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}