{"id":19257,"date":"2024-10-11T14:41:06","date_gmt":"2024-10-11T12:41:06","guid":{"rendered":"https:\/\/sano.science\/?post_type=seminars&#038;p=19257"},"modified":"2025-02-18T20:54:33","modified_gmt":"2025-02-18T19:54:33","slug":"computational-intelligence-development-towards-a-distributed-continuous-learning-architecture","status":"publish","type":"seminars","link":"https:\/\/sano.science\/seminars\/computational-intelligence-development-towards-a-distributed-continuous-learning-architecture\/","title":{"rendered":"145. \u00a0Computational Intelligence Development Towards a Distributed Continuous Learning Architecture\u00a0\u00a0"},"content":{"rendered":"\n<h2 class=\"wp-block-heading eplus-wrapper\" id=\"h-abstract\">Abstract<em>:<\/em><\/h2>\n\n\n\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n<p class=\" eplus-wrapper\">The current direction of artificial intelligence development is focused on scaling data and computing processes. As important as this direction is, it does not provide the explainability that is necessary for sharing the knowledge among peers. This would be very beneficial within the medical world. The patients\u2019 data is distributed among many hospitals, clinics, and research centres and lacks a unified form. Moreover, due to GDPR restrictions, we cannot share such sensitive data freely, and it must be processed locally, which can cause a problem when scientists want to process the data and see a bigger picture. AI models often lack ways to explain itself and to share the knowledge gained from a dataset. Due to limited resources on hospitals premises, we ask ourselves if we can distribute the work on \u201cfast\u201d and \u201cslow\u201d processes, such as classification, and rules discovery and inference, respectively. The standard federated learning methods might be hard to implement into the real-world hospitals and research centres due to serious variations in data, its form and software solutions implemented locally. We hypothesise an architecture that allows to perform computations locally, on hospitals infrastructure, without sharing the patients\u2019 data, but shares the gained knowledge and compare treatment paths and results within the network of research centres and hospitals.<\/p>\n\n\n\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n<h2 class=\"wp-block-heading eplus-wrapper\" id=\"h-about-the-author\">About the author:<\/h2>\n\n\n\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n<p class=\" eplus-wrapper\">Filip completed his Master&#8217;s degree in Computer Science at AGH University in cooperation with Sano. For his thesis, he examined a design of a federated text search engine for Biomedical literature analysis. At Sano he works on Computational Intelligence and Distributed Systems design for medical centres. As part of his career, he is interested in topics such as Cloud Computing, Large Scale Computing and Natural Language Processing.<\/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-large eplus-wrapper\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"536\" src=\"https:\/\/sano.science\/wp-content\/uploads\/2024\/10\/Sano_Seminarium_Filip_Katulski_-1024x536.png\" alt=\"\" class=\"wp-image-19362\" srcset=\"https:\/\/sano.science\/wp-content\/uploads\/2024\/10\/Sano_Seminarium_Filip_Katulski_-1024x536.png 1024w, https:\/\/sano.science\/wp-content\/uploads\/2024\/10\/Sano_Seminarium_Filip_Katulski_-300x157.png 300w, https:\/\/sano.science\/wp-content\/uploads\/2024\/10\/Sano_Seminarium_Filip_Katulski_-768x402.png 768w, https:\/\/sano.science\/wp-content\/uploads\/2024\/10\/Sano_Seminarium_Filip_Katulski_.png 1200w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p class=\" eplus-wrapper\"><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Filip Katulski &#8211; PhD Student in the Personal Health Data Science Team Sano Centre for Computational Medicine, Krakow, Poland<\/p>\n","protected":false},"featured_media":0,"template":"","class_list":["post-19257","seminars","type-seminars","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.4 (Yoast SEO v27.4) - 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As important as this direction is, it does not provide the explainability that is necessary for sharing the knowledge among peers. This would be very beneficial within the medical world. The patients\u2019 data is distributed among many hospitals, clinics, and research centres and lacks a unified form. Moreover, due to GDPR restrictions, we cannot share such sensitive data freely, and it must be processed locally, which can cause a problem when scientists want to process the data and see a bigger picture. AI models often lack ways to explain itself and to share the knowledge gained from a dataset. Due to limited resources on hospitals premises, we ask ourselves if we can distribute the work on \u201cfast\u201d and \u201cslow\u201d processes, such as classification, and rules discovery and inference, respectively. The standard federated learning methods might be hard to implement into the real-world hospitals and research centres due to serious variations in data, its form and software solutions implemented locally. We hypothesise an architecture that allows to perform computations locally, on hospitals infrastructure, without sharing the patients\u2019 data, but shares the gained knowledge and compare treatment paths and results within the network of research centres and hospitals.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">The current direction of artificial intelligence development is focused on scaling data and computing processes. As important as this direction is, it does not provide the explainability that is necessary for sharing the knowledge among peers. This would be very beneficial within the medical world. The patients\u2019 data is distributed among many hospitals, clinics, and research centres and lacks a unified form. Moreover, due to GDPR restrictions, we cannot share such sensitive data freely, and it must be processed locally, which can cause a problem when scientists want to process the data and see a bigger picture. AI models often lack ways to explain itself and to share the knowledge gained from a dataset. Due to limited resources on hospitals premises, we ask ourselves if we can distribute the work on \u201cfast\u201d and \u201cslow\u201d processes, such as classification, and rules discovery and inference, respectively. The standard federated learning methods might be hard to implement into the real-world hospitals and research centres due to serious variations in data, its form and software solutions implemented locally. We hypothesise an architecture that allows to perform computations locally, on hospitals infrastructure, without sharing the patients\u2019 data, but shares the gained knowledge and compare treatment paths and results within the network of research centres and hospitals.<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"30px","epAnimationGeneratedClass":"edplus_anim-rF2kpK","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-yvJ3ac","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<h2 class=\"wp-block-heading eplus-wrapper\" id=\"h-about-the-author\">About the author:<\/h2>\n","innerContent":["\n<h2 class=\"wp-block-heading eplus-wrapper\" id=\"h-about-the-author\">About the author:<\/h2>\n"]},{"blockName":"core\/spacer","attrs":{"height":"30px","epAnimationGeneratedClass":"edplus_anim-rF2kpK","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-x8nODX","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">Filip completed his Master's degree in Computer Science at AGH University in cooperation with Sano. For his thesis, he examined a design of a federated text search engine for Biomedical literature analysis. At Sano he works on Computational Intelligence and Distributed Systems design for medical centres. As part of his career, he is interested in topics such as Cloud Computing, Large Scale Computing and Natural Language Processing.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">Filip completed his Master's degree in Computer Science at AGH University in cooperation with Sano. For his thesis, he examined a design of a federated text search engine for Biomedical literature analysis. At Sano he works on Computational Intelligence and Distributed Systems design for medical centres. As part of his career, he is interested in topics such as Cloud Computing, Large Scale Computing and Natural Language Processing.<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"30px","epAnimationGeneratedClass":"edplus_anim-rF2kpK","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":19362,"sizeSlug":"large","linkDestination":"none","epAnimationGeneratedClass":"edplus_anim-h72DOH","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<figure class=\"wp-block-image size-large eplus-wrapper\"><img src=\"https:\/\/sano.science\/wp-content\/uploads\/2024\/10\/Sano_Seminarium_Filip_Katulski_-1024x536.png\" alt=\"\" class=\"wp-image-19362\"\/><\/figure>\n","innerContent":["\n<figure 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