{"id":15617,"date":"2024-03-04T20:41:58","date_gmt":"2024-03-04T19:41:58","guid":{"rendered":"https:\/\/sano.science\/?post_type=seminars&p=15617"},"modified":"2024-07-24T12:05:08","modified_gmt":"2024-07-24T10:05:08","slug":"application-of-federated-learning-to-medical-data-at-large-scale","status":"publish","type":"seminars","link":"https:\/\/sano.science\/seminars\/application-of-federated-learning-to-medical-data-at-large-scale\/","title":{"rendered":"139. Application of Federated Learning to Medical Data at Large Scale"},"content":{"rendered":"\n
<\/div>\n\n\n\n

Abstract:<\/h2>\n\n\n\n
<\/div>\n\n\n\n

Artificial intelligence is currently one of the fastest-growing fields in science. To achieve high accuracy, modern deep learning models require large and diverse datasets for training. Creating such datasets, especially with medical data, is challenging due to two main factors. 1) The sensitive nature of medical data requires thorough protection and removal of patient-identifying information. 2) Large and diverse datasets are unlikely to be available for free due to the significant effort and time required for their creation and management [1].<\/p>\n\n\n\n

<\/div>\n\n\n\n

For this reason, exploring alternate ways of training neural networks is crucial. Federated learning [2] reverses the standard machine learning paradigm: instead of collecting datasets from various institutions on a single server, only the locally trained models\u2019 parameters are transferred and used to generate the global model. The essential element of the federated learning framework is the aggregation method, which enables the calculation of the central model\u2019s parameters. The quality of the global model on all institutions\u2019 data largely relies on these techniques [1,3].<\/p>\n\n\n\n

<\/div>\n\n\n\n

Numerous studies have been conducted on the application of federated learning in medical imaging, particularly for X-ray images [4]. However, as many articles show, these studies rely on simplistic aggregation algorithms, such as FedAvg [2] or FedProx [5], which may perform poorly with more heterogeneous data. Thus, it is crucial to comprehensively analyze the various existing algorithms, ranging from basic to specialized, for particular applications or data types.<\/p>\n\n\n\n

<\/div>\n\n\n\n

The research aims to analyze the potential application of federated learning to MRI [6] and diffusion MRI data [7]. The results confirm the significance of utilizing federated learning and its advantages, particularly regarding data security and the generalization of deep learning models.<\/p>\n\n\n\n

<\/div>\n\n\n\n

[1] Rieke, N., Hancox, J., Li, W., et al. (2020). The future of digital health with federated learning. Npj Digital Medicine, 3.<\/p>\n\n\n\n

[2] McMahan, B., Moore, E., Ramage, D., et al. (2017). Communication-Efficient Learning of Deep Networks from Decentralized Data. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 273-1282.<\/p>\n\n\n\n

[3] Li, T., Sahu, A.K., Talwalkar, A., Smith, V. (2020). Federated learning: challenges, methods, and future directions. IEEE Signal Processing Magazine, 37(3), 50-60.<\/p>\n\n\n\n

[4] Ciupek, D., Malawski, M., Pi\u0119ciak, T. (2024). Federated Learning: A new frontier in the exploration of heterogeneous data in medical imaging. arXiv (available in July\/August 2024).<\/p>\n\n\n\n

[5] Li, T., Sahu, A.K., Zaheer, M., et al. (2020). Federated optimization in heterogeneous networks. Proceedings of Machine Learning and Systems, 429-450.<\/p>\n\n\n\n

[6] Fiszer, J., Ciupek, D., Malawski, M., Pi\u0119ciak, T. (2024). Federated image-to-image MRI translation from heterogeneous multiple-sites data. Abstract from 2024 ISMRM & SMRT Annual Meeting & Exhibition.<\/p>\n\n\n\n

[7] Ciupek, D., Fiszer, J., Malawski, M., Pi\u0119ciak, T. (2024). Grasping the Microstructural Parameters of the Brain in a Heterogeneous Multi-site Environment: a Federated Learning Approach. Abstract from NEURONUS Neuroscience Forum 2024.<\/p>\n\n\n\n

<\/div>\n\n\n\n

About the author<\/strong><\/h2>\n\n\n\n
<\/div>\n\n\n\n

Dominika Ciupek is a graduate of Biomedical Engineering at the Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering at the AGH University of Krakow. In 2021, she defended her engineering thesis \u201cMulticompartment models in diffusion-relaxometry magnetic resonance imaging\u201d and in 2022, with distinction, her master\u2019s thesis on the analysis of the variability of microstructural parameters along the white matter pathways across the lifespan. Currently, she is pursuing her PhD at Sano as part of the Extreme-scale Data and Computing team.<\/p>\n\n\n\n

<\/div>\n\n\n\n
\"\"<\/figure>\n\n\n\n
<\/div>\n","protected":false},"excerpt":{"rendered":"

Dominika Ciupek \u2013 PhD Student, Extreme-Scale Data and Computing, Sano Centre for Computational Medicine, Krakow, PL<\/p>\n","protected":false},"featured_media":0,"template":"","class_list":["post-15617","seminars","type-seminars","status-publish","hentry"],"yoast_head":"\n139. Application of Federated Learning to Medical Data at Large Scale - 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\/seminars\/application-of-federated-learning-to-medical-data-at-large-scale\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"139. Application of Federated Learning to Medical Data at Large Scale\" \/>\n<meta property=\"og:description\" content=\"Dominika Ciupek \u2013 PhD Student, Extreme-Scale Data and Computing, Sano Centre for Computational Medicine, Krakow, PL\" \/>\n<meta property=\"og:url\" content=\"https:\/\/sano.science\/seminars\/application-of-federated-learning-to-medical-data-at-large-scale\/\" \/>\n<meta property=\"og:site_name\" content=\"Centre for Computational Personalized Medicine\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/sano.science\/\" \/>\n<meta property=\"article:modified_time\" content=\"2024-07-24T10:05:08+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/sano.science\/wp-content\/uploads\/2024\/07\/Seminarium_Domninika_Ciupek-1024x536.png\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:site\" content=\"@sanoscience\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"3 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/sano.science\\\/seminars\\\/application-of-federated-learning-to-medical-data-at-large-scale\\\/\",\"url\":\"https:\\\/\\\/sano.science\\\/seminars\\\/application-of-federated-learning-to-medical-data-at-large-scale\\\/\",\"name\":\"139. Application of Federated Learning to Medical Data at Large Scale - Centre for Computational Personalized Medicine\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/sano.science\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/sano.science\\\/seminars\\\/application-of-federated-learning-to-medical-data-at-large-scale\\\/#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/sano.science\\\/seminars\\\/application-of-federated-learning-to-medical-data-at-large-scale\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/sano.science\\\/wp-content\\\/uploads\\\/2024\\\/07\\\/Seminarium_Domninika_Ciupek-1024x536.png\",\"datePublished\":\"2024-03-04T19:41:58+00:00\",\"dateModified\":\"2024-07-24T10:05:08+00:00\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/sano.science\\\/seminars\\\/application-of-federated-learning-to-medical-data-at-large-scale\\\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/sano.science\\\/seminars\\\/application-of-federated-learning-to-medical-data-at-large-scale\\\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/sano.science\\\/seminars\\\/application-of-federated-learning-to-medical-data-at-large-scale\\\/#primaryimage\",\"url\":\"https:\\\/\\\/sano.science\\\/wp-content\\\/uploads\\\/2024\\\/07\\\/Seminarium_Domninika_Ciupek.png\",\"contentUrl\":\"https:\\\/\\\/sano.science\\\/wp-content\\\/uploads\\\/2024\\\/07\\\/Seminarium_Domninika_Ciupek.png\",\"width\":1200,\"height\":628},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/sano.science\\\/seminars\\\/application-of-federated-learning-to-medical-data-at-large-scale\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/sano.science\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Seminars\",\"item\":\"https:\\\/\\\/sano.science\\\/seminars\\\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"139. Application of Federated Learning to Medical Data at Large Scale\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/sano.science\\\/#website\",\"url\":\"https:\\\/\\\/sano.science\\\/\",\"name\":\"Centre for Computational Personalized Medicine\",\"description\":\"Sano \u2013 Centre for Computational Medicine\",\"publisher\":{\"@id\":\"https:\\\/\\\/sano.science\\\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/sano.science\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"},{\"@type\":\"Organization\",\"@id\":\"https:\\\/\\\/sano.science\\\/#organization\",\"name\":\"Sano \u2013 Centre for Computational Medicine\",\"alternateName\":\"Sano\",\"url\":\"https:\\\/\\\/sano.science\\\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/sano.science\\\/#\\\/schema\\\/logo\\\/image\\\/\",\"url\":\"https:\\\/\\\/sano.science\\\/wp-content\\\/uploads\\\/2024\\\/05\\\/logo_sano_podstawowe.png\",\"contentUrl\":\"https:\\\/\\\/sano.science\\\/wp-content\\\/uploads\\\/2024\\\/05\\\/logo_sano_podstawowe.png\",\"width\":700,\"height\":265,\"caption\":\"Sano \u2013 Centre for Computational Medicine\"},\"image\":{\"@id\":\"https:\\\/\\\/sano.science\\\/#\\\/schema\\\/logo\\\/image\\\/\"},\"sameAs\":[\"https:\\\/\\\/www.facebook.com\\\/sano.science\\\/\",\"https:\\\/\\\/x.com\\\/sanoscience\",\"https:\\\/\\\/www.linkedin.com\\\/company\\\/sanoscience\\\/\",\"https:\\\/\\\/www.youtube.com\\\/channel\\\/UCDZ_8TcjMWUG2ZcgKKgfpwQ\",\"https:\\\/\\\/bsky.app\\\/profile\\\/sanoscience.bsky.social\"]}]}<\/script>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"139. Application of Federated Learning to Medical Data at Large Scale - Centre for Computational Personalized Medicine","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/sano.science\/seminars\/application-of-federated-learning-to-medical-data-at-large-scale\/","og_locale":"en_US","og_type":"article","og_title":"139. Application of Federated Learning to Medical Data at Large Scale","og_description":"Dominika Ciupek \u2013 PhD Student, Extreme-Scale Data and Computing, Sano Centre for Computational Medicine, Krakow, PL","og_url":"https:\/\/sano.science\/seminars\/application-of-federated-learning-to-medical-data-at-large-scale\/","og_site_name":"Centre for Computational Personalized Medicine","article_publisher":"https:\/\/www.facebook.com\/sano.science\/","article_modified_time":"2024-07-24T10:05:08+00:00","og_image":[{"url":"https:\/\/sano.science\/wp-content\/uploads\/2024\/07\/Seminarium_Domninika_Ciupek-1024x536.png","type":"","width":"","height":""}],"twitter_card":"summary_large_image","twitter_site":"@sanoscience","twitter_misc":{"Est. reading time":"3 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/sano.science\/seminars\/application-of-federated-learning-to-medical-data-at-large-scale\/","url":"https:\/\/sano.science\/seminars\/application-of-federated-learning-to-medical-data-at-large-scale\/","name":"139. Application of Federated Learning to Medical Data at Large Scale - Centre for Computational Personalized Medicine","isPartOf":{"@id":"https:\/\/sano.science\/#website"},"primaryImageOfPage":{"@id":"https:\/\/sano.science\/seminars\/application-of-federated-learning-to-medical-data-at-large-scale\/#primaryimage"},"image":{"@id":"https:\/\/sano.science\/seminars\/application-of-federated-learning-to-medical-data-at-large-scale\/#primaryimage"},"thumbnailUrl":"https:\/\/sano.science\/wp-content\/uploads\/2024\/07\/Seminarium_Domninika_Ciupek-1024x536.png","datePublished":"2024-03-04T19:41:58+00:00","dateModified":"2024-07-24T10:05:08+00:00","breadcrumb":{"@id":"https:\/\/sano.science\/seminars\/application-of-federated-learning-to-medical-data-at-large-scale\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/sano.science\/seminars\/application-of-federated-learning-to-medical-data-at-large-scale\/"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/sano.science\/seminars\/application-of-federated-learning-to-medical-data-at-large-scale\/#primaryimage","url":"https:\/\/sano.science\/wp-content\/uploads\/2024\/07\/Seminarium_Domninika_Ciupek.png","contentUrl":"https:\/\/sano.science\/wp-content\/uploads\/2024\/07\/Seminarium_Domninika_Ciupek.png","width":1200,"height":628},{"@type":"BreadcrumbList","@id":"https:\/\/sano.science\/seminars\/application-of-federated-learning-to-medical-data-at-large-scale\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/sano.science\/"},{"@type":"ListItem","position":2,"name":"Seminars","item":"https:\/\/sano.science\/seminars\/"},{"@type":"ListItem","position":3,"name":"139. Application of Federated Learning to Medical Data at Large Scale"}]},{"@type":"WebSite","@id":"https:\/\/sano.science\/#website","url":"https:\/\/sano.science\/","name":"Centre for Computational Personalized Medicine","description":"Sano \u2013 Centre for Computational Medicine","publisher":{"@id":"https:\/\/sano.science\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/sano.science\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Organization","@id":"https:\/\/sano.science\/#organization","name":"Sano \u2013 Centre for Computational Medicine","alternateName":"Sano","url":"https:\/\/sano.science\/","logo":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/sano.science\/#\/schema\/logo\/image\/","url":"https:\/\/sano.science\/wp-content\/uploads\/2024\/05\/logo_sano_podstawowe.png","contentUrl":"https:\/\/sano.science\/wp-content\/uploads\/2024\/05\/logo_sano_podstawowe.png","width":700,"height":265,"caption":"Sano \u2013 Centre for Computational Medicine"},"image":{"@id":"https:\/\/sano.science\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/www.facebook.com\/sano.science\/","https:\/\/x.com\/sanoscience","https:\/\/www.linkedin.com\/company\/sanoscience\/","https:\/\/www.youtube.com\/channel\/UCDZ_8TcjMWUG2ZcgKKgfpwQ","https:\/\/bsky.app\/profile\/sanoscience.bsky.social"]}]}},"acf":[],"gutenberg_blocks":[{"blockName":"custom-styles","attrs":{"styles":""}},{"blockName":"core\/spacer","attrs":{"height":"50px","epAnimationGeneratedClass":"edplus_anim-RemKmd","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/heading","attrs":{"epAnimationGeneratedClass":"edplus_anim-gAvnUQ","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<h2 class=\"wp-block-heading eplus-wrapper\">Abstract:<\/h2>\n","innerContent":["\n<h2 class=\"wp-block-heading eplus-wrapper\">Abstract:<\/h2>\n"]},{"blockName":"core\/spacer","attrs":{"height":"30px","epAnimationGeneratedClass":"edplus_anim-RemKmd","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\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-YuYu77","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">Artificial intelligence is currently one of the fastest-growing fields in science. To achieve high accuracy, modern deep learning models require large and diverse datasets for training. Creating such datasets, especially with medical data, is challenging due to two main factors. 1) The sensitive nature of medical data requires thorough protection and removal of patient-identifying information. 2) Large and diverse datasets are unlikely to be available for free due to the significant effort and time required for their creation and management [1].<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">Artificial intelligence is currently one of the fastest-growing fields in science. To achieve high accuracy, modern deep learning models require large and diverse datasets for training. Creating such datasets, especially with medical data, is challenging due to two main factors. 1) The sensitive nature of medical data requires thorough protection and removal of patient-identifying information. 2) Large and diverse datasets are unlikely to be available for free due to the significant effort and time required for their creation and management [1].<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"30px","epAnimationGeneratedClass":"edplus_anim-RemKmd","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\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-xAILUw","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">For this reason, exploring alternate ways of training neural networks is crucial. Federated learning [2] reverses the standard machine learning paradigm: instead of collecting datasets from various institutions on a single server, only the locally trained models\u2019 parameters are transferred and used to generate the global model. The essential element of the federated learning framework is the aggregation method, which enables the calculation of the central model\u2019s parameters. The quality of the global model on all institutions\u2019 data largely relies on these techniques [1,3].<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">For this reason, exploring alternate ways of training neural networks is crucial. Federated learning [2] reverses the standard machine learning paradigm: instead of collecting datasets from various institutions on a single server, only the locally trained models\u2019 parameters are transferred and used to generate the global model. The essential element of the federated learning framework is the aggregation method, which enables the calculation of the central model\u2019s parameters. The quality of the global model on all institutions\u2019 data largely relies on these techniques [1,3].<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"30px","epAnimationGeneratedClass":"edplus_anim-RemKmd","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\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-2yKfKD","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">Numerous studies have been conducted on the application of federated learning in medical imaging, particularly for X-ray images [4]. However, as many articles show, these studies rely on simplistic aggregation algorithms, such as FedAvg [2] or FedProx [5], which may perform poorly with more heterogeneous data. Thus, it is crucial to comprehensively analyze the various existing algorithms, ranging from basic to specialized, for particular applications or data types.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">Numerous studies have been conducted on the application of federated learning in medical imaging, particularly for X-ray images [4]. However, as many articles show, these studies rely on simplistic aggregation algorithms, such as FedAvg [2] or FedProx [5], which may perform poorly with more heterogeneous data. Thus, it is crucial to comprehensively analyze the various existing algorithms, ranging from basic to specialized, for particular applications or data types.<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"30px","epAnimationGeneratedClass":"edplus_anim-RemKmd","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\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-srO7M7","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">The research aims to analyze the potential application of federated learning to MRI [6] and diffusion MRI data [7]. The results confirm the significance of utilizing federated learning and its advantages, particularly regarding data security and the generalization of deep learning models.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">The research aims to analyze the potential application of federated learning to MRI [6] and diffusion MRI data [7]. The results confirm the significance of utilizing federated learning and its advantages, particularly regarding data security and the generalization of deep learning models.<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"30px","epAnimationGeneratedClass":"edplus_anim-RemKmd","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\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-50RwTH","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">[1] Rieke, N., Hancox, J., Li, W., et al. (2020). The future of digital health with federated learning. Npj Digital Medicine, 3.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">[1] Rieke, N., Hancox, J., Li, W., et al. (2020). The future of digital health with federated learning. Npj Digital Medicine, 3.<\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-uvwGAC","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">[2] McMahan, B., Moore, E., Ramage, D., et al. (2017). Communication-Efficient Learning of Deep Networks from Decentralized Data. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 273-1282.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">[2] McMahan, B., Moore, E., Ramage, D., et al. (2017). Communication-Efficient Learning of Deep Networks from Decentralized Data. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 273-1282.<\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-XMP2vO","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">[3] Li, T., Sahu, A.K., Talwalkar, A., Smith, V. (2020). Federated learning: challenges, methods, and future directions. IEEE Signal Processing Magazine, 37(3), 50-60.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">[3] Li, T., Sahu, A.K., Talwalkar, A., Smith, V. (2020). Federated learning: challenges, methods, and future directions. IEEE Signal Processing Magazine, 37(3), 50-60.<\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-P9zAEp","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">[4] Ciupek, D., Malawski, M., Pi\u0119ciak, T. (2024). Federated Learning: A new frontier in the exploration of heterogeneous data in medical imaging. arXiv (available in July\/August 2024).<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">[4] Ciupek, D., Malawski, M., Pi\u0119ciak, T. (2024). Federated Learning: A new frontier in the exploration of heterogeneous data in medical imaging. arXiv (available in July\/August 2024).<\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-FB4ehN","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">[5] Li, T., Sahu, A.K., Zaheer, M., et al. (2020). Federated optimization in heterogeneous networks. Proceedings of Machine Learning and Systems, 429-450.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">[5] Li, T., Sahu, A.K., Zaheer, M., et al. (2020). Federated optimization in heterogeneous networks. Proceedings of Machine Learning and Systems, 429-450.<\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-6A6B3E","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">[6] Fiszer, J., Ciupek, D., Malawski, M., Pi\u0119ciak, T. (2024). Federated image-to-image MRI translation from heterogeneous multiple-sites data. Abstract from 2024 ISMRM & SMRT Annual Meeting & Exhibition.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">[6] Fiszer, J., Ciupek, D., Malawski, M., Pi\u0119ciak, T. (2024). Federated image-to-image MRI translation from heterogeneous multiple-sites data. Abstract from 2024 ISMRM & SMRT Annual Meeting & Exhibition.<\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-Fgb6oW","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">[7] Ciupek, D., Fiszer, J., Malawski, M., Pi\u0119ciak, T. (2024). Grasping the Microstructural Parameters of the Brain in a Heterogeneous Multi-site Environment: a Federated Learning Approach. Abstract from NEURONUS Neuroscience Forum 2024.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">[7] Ciupek, D., Fiszer, J., Malawski, M., Pi\u0119ciak, T. (2024). Grasping the Microstructural Parameters of the Brain in a Heterogeneous Multi-site Environment: a Federated Learning Approach. Abstract from NEURONUS Neuroscience Forum 2024.<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"30px","epAnimationGeneratedClass":"edplus_anim-RemKmd","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\/heading","attrs":{"epAnimationGeneratedClass":"edplus_anim-zvK6AK","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<h2 class=\"wp-block-heading eplus-wrapper\"><strong>About the author<\/strong><\/h2>\n","innerContent":["\n<h2 class=\"wp-block-heading eplus-wrapper\"><strong>About the author<\/strong><\/h2>\n"]},{"blockName":"core\/spacer","attrs":{"height":"30px","epAnimationGeneratedClass":"edplus_anim-RemKmd","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\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-CmwJRa","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">Dominika Ciupek is a graduate of Biomedical Engineering at the Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering at the AGH University of Krakow. In 2021, she defended her engineering thesis \u201cMulticompartment models in diffusion-relaxometry magnetic resonance imaging\u201d and in 2022, with distinction, her master\u2019s thesis on the analysis of the variability of microstructural parameters along the white matter pathways across the lifespan. Currently, she is pursuing her PhD at Sano as part of the Extreme-scale Data and Computing team.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">Dominika Ciupek is a graduate of Biomedical Engineering at the Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering at the AGH University of Krakow. In 2021, she defended her engineering thesis \u201cMulticompartment models in diffusion-relaxometry magnetic resonance imaging\u201d and in 2022, with distinction, her master\u2019s thesis on the analysis of the variability of microstructural parameters along the white matter pathways across the lifespan. Currently, she is pursuing her PhD at Sano as part of the Extreme-scale Data and Computing team.<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"50px","epAnimationGeneratedClass":"edplus_anim-RemKmd","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/image","attrs":{"id":18306,"sizeSlug":"large","linkDestination":"none","epAnimationGeneratedClass":"edplus_anim-82UBM5","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<figure class=\"wp-block-image size-large eplus-wrapper\"><img src=\"https:\/\/sano.science\/wp-content\/uploads\/2024\/07\/Seminarium_Domninika_Ciupek-1024x536.png\" alt=\"\" class=\"wp-image-18306\"\/><\/figure>\n","innerContent":["\n<figure class=\"wp-block-image size-large eplus-wrapper\"><img src=\"https:\/\/sano.science\/wp-content\/uploads\/2024\/07\/Seminarium_Domninika_Ciupek-1024x536.png\" alt=\"\" class=\"wp-image-18306\"\/><\/figure>\n"]},{"blockName":"core\/spacer","attrs":{"height":"30px","epAnimationGeneratedClass":"edplus_anim-RemKmd","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"]}],"meta_data":{"event_day":"2024-07-01","event_time":"2:00-3:30 PM (CEST)","event_guest":"Dominika Ciupek \u2013 PhD Student, Extreme-Scale Data and Computing, Sano Centre for Computational Medicine, Krakow, PL","has_medias":true,"medias":[{"icon":{"ID":1144,"id":1144,"title":"clock","filename":"clock.svg","filesize":1479,"url":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/clock.svg","link":"https:\/\/sano.science\/seminars\/79-digital-behaviour-change-interventions-dbci-from-design-to-implementation\/clock\/","alt":"clock Sano Seminar","author":"7","description":"","caption":"Sano Seminar clock","name":"clock","status":"inherit","uploaded_to":13471,"date":"2023-06-01 13:24:42","modified":"2024-10-09 16:41:04","menu_order":0,"mime_type":"image\/svg+xml","type":"image","subtype":"svg+xml","icon":"https:\/\/sano.science\/wp-includes\/images\/media\/default.png","width":56,"height":57,"sizes":{"thumbnail":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/clock.svg","thumbnail-width":147,"thumbnail-height":150,"medium":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/clock.svg","medium-width":294,"medium-height":300,"medium_large":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/clock.svg","medium_large-width":768,"medium_large-height":783,"large":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/clock.svg","large-width":1004,"large-height":1024,"1536x1536":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/clock.svg","1536x1536-width":56,"1536x1536-height":57,"2048x2048":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/clock.svg","2048x2048-width":56,"2048x2048-height":57}},"title":"1st July 2024, 2:00-3:30 PM (CEST)","link":""},{"icon":{"ID":1146,"id":1146,"title":"camera","filename":"camera.svg","filesize":1129,"url":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/camera.svg","link":"https:\/\/sano.science\/seminars\/79-digital-behaviour-change-interventions-dbci-from-design-to-implementation\/camera\/","alt":"camera Sano Seminar","author":"7","description":"","caption":"Sano Seminar camera","name":"camera","status":"inherit","uploaded_to":13471,"date":"2023-06-01 13:25:24","modified":"2024-10-09 16:42:29","menu_order":0,"mime_type":"image\/svg+xml","type":"image","subtype":"svg+xml","icon":"https:\/\/sano.science\/wp-includes\/images\/media\/default.png","width":60,"height":38,"sizes":{"thumbnail":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/camera.svg","thumbnail-width":150,"thumbnail-height":95,"medium":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/camera.svg","medium-width":300,"medium-height":190,"medium_large":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/camera.svg","medium_large-width":768,"medium_large-height":486,"large":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/camera.svg","large-width":1024,"large-height":648,"1536x1536":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/camera.svg","1536x1536-width":60,"1536x1536-height":38,"2048x2048":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/camera.svg","2048x2048-width":60,"2048x2048-height":38}},"title":"Join via ZOOM on","link":{"title":"seminar.sano.science","url":"https:\/\/us06web.zoom.us\/j\/81263292238#success","target":"_blank"}}]},"_links":{"self":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/seminars\/15617","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/seminars"}],"about":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/types\/seminars"}],"version-history":[{"count":17,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/seminars\/15617\/revisions"}],"predecessor-version":[{"id":18308,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/seminars\/15617\/revisions\/18308"}],"wp:attachment":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media?parent=15617"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}