{"id":27318,"date":"2025-09-28T17:34:48","date_gmt":"2025-09-28T15:34:48","guid":{"rendered":"https:\/\/sano.science\/?p=27318"},"modified":"2025-12-08T16:27:43","modified_gmt":"2025-12-08T15:27:43","slug":"strong-sano-representation-at-the-prestigious-miccai-2025-conference","status":"publish","type":"post","link":"https:\/\/sano.science\/strong-sano-representation-at-the-prestigious-miccai-2025-conference\/","title":{"rendered":"Strong Sano Representation at the Prestigious MICCAI 2025 Conference\u00a0"},"content":{"rendered":"\n

This year\u2019s International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2025)<\/em> brought together leading experts in artificial intelligence, medical imaging, and computational medicine. Among them was an exceptionally strong representation from the Sano Centre for Computational Medicine \u2014 researchers from our Extreme-Scale Data and Computing team and the Medical Imaging and Robotics team \u2014 who shared their latest scientific results and took part in key sessions and workshops. <\/p>\n\n\n\n

Federated Learning for Brain Tumor Segmentation<\/strong> <\/p>\n\n\n\n

At the DeCaF Workshop on Distributed, Collaborative and Federated Learning<\/em>, Jan Fiszer<\/strong> presented the paper \u201cValidation of Various Normalization Methods for Brain Tumor Segmentation: Can Federated Learning Overcome This Heterogeneity?\u201d<\/em>, co-authored with Dominika Ciupek<\/strong> and Maciej Malawski<\/strong>. The full paper is available at: https:\/\/arxiv.org\/abs\/2510.07126. <\/p>\n\n\n\n

The DeCaF workshop provided a dedicated platform for discussing methodological advances in distributed, collaborative, and federated learning approaches applied to medical data. Jan\u2019s presentation contributed to the growing field of AI-powered collaboration in medical imaging, addressing the challenges of data heterogeneity and privacy in multi-institutional research. <\/p>\n\n\n\n

Statistical Shape Analysis in Deep Vein Thrombosis<\/strong> <\/p>\n\n\n\n

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Magdalena Otta<\/strong> represented Sano with a poster titled \u201cShape vs Flow: A 2D Statistical Shape Analysis of the Projection of Common Iliac Veins in Patients with Deep Vein Thrombosis\u201d<\/em> during the Shape in Medical Imaging (ShapeMI)<\/em> workshop.\u00a0\u00a0<\/p>\n\n\n\n

The study, co-authored with Karol Zaj\u0105c<\/strong>, Maciej Malawski<\/strong>, Ian Halliday<\/strong>, Chung Lim<\/strong>, Janice Tsui<\/strong>, and Andrew Narracott<\/strong>, explored how geometric analysis could reveal clinically relevant differences in venous anatomy. The research offered new insights into patient-specific vascular modeling and was further detailed in a comprehensive video presentation<\/a>.<\/p>\n<\/div>\n<\/div>\n\n\n

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The ShapeMI workshop serves as a venue for presenting state-of-the-art methods and applications in shape analysis<\/em> and geometric learning<\/em> in medical imaging. It brings together experts exploring how the shape and geometry of anatomical structures can be modeled, analyzed, and applied in medical contexts. With the growing availability of 3D and time-varying imaging data, the workshop emphasizes the importance of shape-based and spectral analysis as tools that offer greater sensitivity to subtle local variations than traditional structural measures. <\/p>\n<\/blockquote>\n\n\n\n

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Polish Contributions by Sano Researchers<\/strong> <\/p>\n\n\n\n

All publications from Poland presented at MICCAI 2025 listed first authors from Sano\u2019s Extreme-Scale Data and Computing team and Medical Imaging and Robotics team \u2014 including Joanna Kaleta<\/strong>, Magdalena Otta<\/strong>, Micha\u0142 Grzeszczyk<\/strong>, Jan Fiszer<\/strong>, and Tomasz Szczepa\u0144ski<\/strong>. <\/p>\n\n\n\n

In the poster session, Micha\u0142 Grzeszczyk<\/strong> presented his work \u201cRegScore: Scoring Systems for Regression Tasks\u201d<\/em>, which introduced interpretable and personalized regression models combining imaging and tabular data. The paper was available on arXiv.org<\/em>, with open-source code shared on GitHub<\/em>. <\/p>\n\n\n\n

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Sano\u2019s presence was further highlighted by the poster featuring Tomasz Szczepa\u0144ski<\/strong>\u2019s research, \u201cGEPAR3D: Geometry Prior-Assisted Learning for 3D Tooth Segmentation\u201d<\/em>, which proposed a geometry-assisted deep learning method integrating instance detection and multiclass segmentation in dental Conical-Beam Computed Tomography CBCT scans. By integrating statistical shape priors, the method encourages anatomical consistency and substantially improves root segmentation, particularly in the apical root regions, which are critical for assessing root resorption during orthodontic treatment. The project, including code and an interactive 3D visualization demo, is available online at the <\/em>project site<\/em><\/a>. <\/p>\n\n\n\n

Sano\u2019s contributions were also represented by the poster presented by Joanna Kaleta \u2014 \u201cMachine Learning-Based Decision Support for Allergy Diagnosis: Real-World Implementation in a Hospital Setting\u201d.<\/p>\n\n\n\n

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Ongoing Training and Skill Development<\/strong>\u00a0<\/p>\n\n\n\n

Beyond presentations, Dominika Ciupek<\/strong> took part in specialized training sessions on AI bias and uncertainty analysis. Her participation underscored Sano\u2019s commitment to continuous skill development for our scientists and to conducting responsible AI research.\u00a0<\/p>\n<\/div>\n<\/div>","protected":false},"excerpt":"This year\u2019s International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2025) brought together leading experts in artificial intelligence, medical imaging, and computational medicine. Among them was an exceptionally strong representation from the Sano Centre for Computational Medicine \u2014 researchers from our Extreme-Scale Data and Computing team and the Medical Imaging and Robotics […]","author":8,"featured_media":27363,"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-27318","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"yoast_head":"\nStrong Sano Representation at the Prestigious MICCAI 2025 Conference\u00a0 - 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\/strong-sano-representation-at-the-prestigious-miccai-2025-conference\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Strong Sano Representation at the Prestigious MICCAI 2025 Conference\u00a0\" \/>\n<meta property=\"og:description\" content=\"This year\u2019s International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2025) brought together leading experts in artificial intelligence, medical imaging, and computational medicine. 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Among them was an exceptionally strong representation from the Sano Centre for Computational Medicine \u2014 researchers from our Extreme-Scale Data and Computing team and the Medical Imaging and Robotics team \u2014 who shared their latest scientific results and took part in key sessions and workshops. <\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">This year\u2019s <em>International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2025)<\/em> brought together leading experts in artificial intelligence, medical imaging, and computational medicine. Among them was an exceptionally strong representation from the Sano Centre for Computational Medicine \u2014 researchers from our Extreme-Scale Data and Computing team and the Medical Imaging and Robotics team \u2014 who shared their latest scientific results and took part in key sessions and workshops. <\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-m5qdkP","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\"><strong>Federated Learning for Brain Tumor Segmentation<\/strong> <\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\"><strong>Federated Learning for Brain Tumor Segmentation<\/strong> <\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-6NVynE","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">At the <em>DeCaF Workshop on Distributed, Collaborative and Federated Learning<\/em>, <strong>Jan Fiszer<\/strong> presented the paper <em>\u201cValidation of Various Normalization Methods for Brain Tumor Segmentation: Can Federated Learning Overcome This Heterogeneity?\u201d<\/em>, co-authored with <strong>Dominika Ciupek<\/strong> and <strong>Maciej Malawski<\/strong>. The full paper is available at: https:\/\/arxiv.org\/abs\/2510.07126. <\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">At the <em>DeCaF Workshop on Distributed, Collaborative and Federated Learning<\/em>, <strong>Jan Fiszer<\/strong> presented the paper <em>\u201cValidation of Various Normalization Methods for Brain Tumor Segmentation: Can Federated Learning Overcome This Heterogeneity?\u201d<\/em>, co-authored with <strong>Dominika Ciupek<\/strong> and <strong>Maciej Malawski<\/strong>. The full paper is available at: https:\/\/arxiv.org\/abs\/2510.07126. <\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-givdzB","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">The DeCaF workshop provided a dedicated platform for discussing methodological advances in distributed, collaborative, and federated learning approaches applied to medical data. Jan\u2019s presentation contributed to the growing field of AI-powered collaboration in medical imaging, addressing the challenges of data heterogeneity and privacy in multi-institutional research. <\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">The DeCaF workshop provided a dedicated platform for discussing methodological advances in distributed, collaborative, and federated learning approaches applied to medical data. Jan\u2019s presentation contributed to the growing field of AI-powered collaboration in medical imaging, addressing the challenges of data heterogeneity and privacy in multi-institutional research. <\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-6wlryV","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\"><strong>Statistical Shape Analysis in Deep Vein Thrombosis<\/strong> <\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\"><strong>Statistical Shape Analysis in Deep Vein Thrombosis<\/strong> <\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"10px","epAnimationGeneratedClass":"edplus_anim-BA3phF","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\/columns","attrs":{"epStylingOptions":{"epCustomColumnsResponsiveEnabled":true,"epCustomColumnsReverseResponsiveEnabled":true,"epCustomColumnsSpacingResponsiveEnabled":true,"epCustomColumns":{"target":"wp-block-column","responsive":true,"hover":false,"options":[{"custom":true,"control":"ColumnToggle"},{"label":"Responsive Columns","control":"Range","attribute":"epCustomColumns","defaults":{"tablet":"2","mobile":"1"},"css":"flex-basis","customValue":"calc(( 100% - 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The research offered new insights into patient-specific vascular modeling and was further detailed in a comprehensive <a href=\"https:\/\/drive.google.com\/file\/d\/1amU_9-ABZFRLJ_GsGq_9BayEQbC0K482\/view\">video presentation<\/a>.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">The study, co-authored with <strong>Karol Zaj\u0105c<\/strong>, <strong>Maciej Malawski<\/strong>, <strong>Ian Halliday<\/strong>, <strong>Chung Lim<\/strong>, <strong>Janice Tsui<\/strong>, and <strong>Andrew Narracott<\/strong>, explored how geometric analysis could reveal clinically relevant differences in venous anatomy. The research offered new insights into patient-specific vascular modeling and was further detailed in a comprehensive <a href=\"https:\/\/drive.google.com\/file\/d\/1amU_9-ABZFRLJ_GsGq_9BayEQbC0K482\/view\">video presentation<\/a>.<\/p>\n"]}],"innerHTML":"\n<div class=\"wp-block-column is-vertically-aligned-center eplus-wrapper\">\n\n<\/div>\n","innerContent":["\n<div class=\"wp-block-column is-vertically-aligned-center eplus-wrapper\">",null,"\n\n",null,"<\/div>\n"]}],"innerHTML":"<div class=\"wp-block-columns eplus-wrapper eplus-styles-uid-e37a7a\">\n\n<\/div>","innerContent":["\n<div class=\"wp-block-columns eplus-wrapper\">",null,"\n\n",null,"<\/div>\n"]},{"blockName":"core\/spacer","attrs":{"height":"10px","epAnimationGeneratedClass":"edplus_anim-E2bcWT","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\/quote","attrs":{"epAnimationGeneratedClass":"edplus_anim-9wKMzz","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-botCXX","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">The ShapeMI workshop serves as a venue for presenting state-of-the-art methods and applications in <em>shape analysis<\/em> and <em>geometric learning<\/em> in medical imaging. It brings together experts exploring how the shape and geometry of anatomical structures can be modeled, analyzed, and applied in medical contexts. With the growing availability of 3D and time-varying imaging data, the workshop emphasizes the importance of shape-based and spectral analysis as tools that offer greater sensitivity to subtle local variations than traditional structural measures. <\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">The ShapeMI workshop serves as a venue for presenting state-of-the-art methods and applications in <em>shape analysis<\/em> and <em>geometric learning<\/em> in medical imaging. It brings together experts exploring how the shape and geometry of anatomical structures can be modeled, analyzed, and applied in medical contexts. With the growing availability of 3D and time-varying imaging data, the workshop emphasizes the importance of shape-based and spectral analysis as tools that offer greater sensitivity to subtle local variations than traditional structural measures. <\/p>\n"]}],"innerHTML":"\n<blockquote class=\"wp-block-quote eplus-wrapper\"><\/blockquote>\n","innerContent":["\n<blockquote class=\"wp-block-quote eplus-wrapper\">",null,"<\/blockquote>\n"]},{"blockName":"core\/spacer","attrs":{"height":"10px","epAnimationGeneratedClass":"edplus_anim-lbSXCf","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-VN9GGl","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\"><strong>Polish Contributions by Sano Researchers<\/strong> <\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\"><strong>Polish Contributions by Sano Researchers<\/strong> <\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-wNXcYB","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">All publications from Poland presented at MICCAI 2025 listed first authors from Sano\u2019s Extreme-Scale Data and Computing team and Medical Imaging and Robotics team \u2014 including <strong>Joanna Kaleta<\/strong>, <strong>Magdalena Otta<\/strong>, <strong>Micha\u0142 Grzeszczyk<\/strong>, <strong>Jan Fiszer<\/strong>, and <strong>Tomasz Szczepa\u0144ski<\/strong>. <\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">All publications from Poland presented at MICCAI 2025 listed first authors from Sano\u2019s Extreme-Scale Data and Computing team and Medical Imaging and Robotics team \u2014 including <strong>Joanna Kaleta<\/strong>, <strong>Magdalena Otta<\/strong>, <strong>Micha\u0142 Grzeszczyk<\/strong>, <strong>Jan Fiszer<\/strong>, and <strong>Tomasz Szczepa\u0144ski<\/strong>. <\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-VN9GGl","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">In the poster session, <strong>Micha\u0142 Grzeszczyk<\/strong> presented his work <em>\u201cRegScore: Scoring Systems for Regression Tasks\u201d<\/em>, which introduced interpretable and personalized regression models combining imaging and tabular data. The paper was available on <em>arXiv.org<\/em>, with open-source code shared on <em>GitHub<\/em>. <\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">In the poster session, <strong>Micha\u0142 Grzeszczyk<\/strong> presented his work <em>\u201cRegScore: Scoring Systems for Regression Tasks\u201d<\/em>, which introduced interpretable and personalized regression models combining imaging and tabular data. The paper was available on <em>arXiv.org<\/em>, with open-source code shared on <em>GitHub<\/em>. <\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"10px","epAnimationGeneratedClass":"edplus_anim-Dxl8OP","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\/columns","attrs":{"epStylingOptions":{"epCustomColumnsResponsiveEnabled":true,"epCustomColumnsReverseResponsiveEnabled":true,"epCustomColumnsSpacingResponsiveEnabled":true,"epCustomColumns":{"target":"wp-block-column","responsive":true,"hover":false,"options":[{"custom":true,"control":"ColumnToggle"},{"label":"Responsive Columns","control":"Range","attribute":"epCustomColumns","defaults":{"tablet":"2","mobile":"1"},"css":"flex-basis","customValue":"calc(( 100% - 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class=\"wp-block-image size-full eplus-wrapper\"><img src=\"https:\/\/sano.science\/wp-content\/uploads\/2025\/12\/Grzeszczyk.jpeg\" alt=\"\" class=\"wp-image-27320\"\/><\/figure>\n","innerContent":["\n<figure class=\"wp-block-image size-full eplus-wrapper\"><img src=\"https:\/\/sano.science\/wp-content\/uploads\/2025\/12\/Grzeszczyk.jpeg\" alt=\"\" class=\"wp-image-27320\"\/><\/figure>\n"]}],"innerHTML":"\n<div class=\"wp-block-column eplus-wrapper\"><\/div>\n","innerContent":["\n<div class=\"wp-block-column eplus-wrapper\">",null,"<\/div>\n"]},{"blockName":"core\/column","attrs":{"epAnimationGeneratedClass":"edplus_anim-FFazSm","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[{"blockName":"core\/image","attrs":{"id":27321,"sizeSlug":"large","linkDestination":"none","epAnimationGeneratedClass":"edplus_anim-A7CRKc","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<figure class=\"wp-block-image size-large eplus-wrapper\"><img src=\"https:\/\/sano.science\/wp-content\/uploads\/2025\/12\/Szczepanski-725x1024.jpeg\" alt=\"\" class=\"wp-image-27321\"\/><\/figure>\n","innerContent":["\n<figure class=\"wp-block-image size-large eplus-wrapper\"><img src=\"https:\/\/sano.science\/wp-content\/uploads\/2025\/12\/Szczepanski-725x1024.jpeg\" alt=\"\" class=\"wp-image-27321\"\/><\/figure>\n"]}],"innerHTML":"\n<div class=\"wp-block-column eplus-wrapper\"><\/div>\n","innerContent":["\n<div class=\"wp-block-column eplus-wrapper\">",null,"<\/div>\n"]}],"innerHTML":"<div class=\"wp-block-columns eplus-wrapper eplus-styles-uid-fe263e\">\n\n<\/div>","innerContent":["\n<div class=\"wp-block-columns eplus-wrapper\">",null,"\n\n",null,"<\/div>\n"]},{"blockName":"core\/spacer","attrs":{"height":"10px","epAnimationGeneratedClass":"edplus_anim-SHtAA4","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-YSPUwy","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">Sano\u2019s presence was further highlighted by the poster featuring <strong>Tomasz Szczepa\u0144ski<\/strong>\u2019s research, <em>\u201cGEPAR3D: Geometry Prior-Assisted Learning for 3D Tooth Segmentation\u201d<\/em>, which proposed a geometry-assisted deep learning method integrating instance detection and multiclass segmentation in dental Conical-Beam Computed Tomography CBCT scans. By integrating statistical shape priors, the method encourages anatomical consistency and substantially improves root segmentation, particularly in the apical root regions, which are critical for assessing root resorption during orthodontic treatment. The project, including code and an interactive 3D visualization demo, is available online at the<em> <\/em><a href=\"https:\/\/tomek1911.github.io\/GEPAR3D\/\" target=\"_blank\" rel=\"noreferrer noopener\"><em>project site<\/em><\/a>. <\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">Sano\u2019s presence was further highlighted by the poster featuring <strong>Tomasz Szczepa\u0144ski<\/strong>\u2019s research, <em>\u201cGEPAR3D: Geometry Prior-Assisted Learning for 3D Tooth Segmentation\u201d<\/em>, which proposed a geometry-assisted deep learning method integrating instance detection and multiclass segmentation in dental Conical-Beam Computed Tomography CBCT scans. By integrating statistical shape priors, the method encourages anatomical consistency and substantially improves root segmentation, particularly in the apical root regions, which are critical for assessing root resorption during orthodontic treatment. The project, including code and an interactive 3D visualization demo, is available online at the<em> <\/em><a href=\"https:\/\/tomek1911.github.io\/GEPAR3D\/\" target=\"_blank\" rel=\"noreferrer noopener\"><em>project site<\/em><\/a>. <\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-Pu2vO0","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">Sano\u2019s contributions were also represented by the poster presented by Joanna Kaleta \u2014 \u201cMachine Learning-Based Decision Support for Allergy Diagnosis: Real-World Implementation in a Hospital Setting\u201d.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">Sano\u2019s contributions were also represented by the poster presented by Joanna Kaleta \u2014 \u201cMachine Learning-Based Decision Support for Allergy Diagnosis: Real-World Implementation in a Hospital Setting\u201d.<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"10px","epAnimationGeneratedClass":"edplus_anim-vT4LUy","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\/columns","attrs":{"epStylingOptions":{"epCustomColumnsResponsiveEnabled":true,"epCustomColumnsReverseResponsiveEnabled":true,"epCustomColumnsSpacingResponsiveEnabled":true,"epCustomColumns":{"target":"wp-block-column","responsive":true,"hover":false,"options":[{"custom":true,"control":"ColumnToggle"},{"label":"Responsive Columns","control":"Range","attribute":"epCustomColumns","defaults":{"tablet":"2","mobile":"1"},"css":"flex-basis","customValue":"calc(( 100% - 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