{"id":15611,"date":"2024-03-04T20:40:07","date_gmt":"2024-03-04T19:40:07","guid":{"rendered":"https:\/\/sano.science\/?post_type=seminars&#038;p=15611"},"modified":"2024-06-05T20:00:01","modified_gmt":"2024-06-05T18:00:01","slug":"advancing-noninvasive-diagnostics-through-the-fusion-of-imaging-and-clinical-data","status":"publish","type":"seminars","link":"https:\/\/sano.science\/seminars\/advancing-noninvasive-diagnostics-through-the-fusion-of-imaging-and-clinical-data\/","title":{"rendered":"137. Advancing Noninvasive Diagnostics Through the Fusion of Imaging and Clinical Data"},"content":{"rendered":"\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n<h2 class=\"wp-block-heading eplus-wrapper\">Abstract<\/h2>\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n<p class=\"eplus-wrapper wp-block-paragraph\">Many clinical procedures involve collecting data samples in the form of imaging and tabular data. To enhance predictive capabilities, novel deep learning architectures are emerging, aiming to fuse information from both sources of information. Still, most solutions are based on simple concatenation or affine transformation mechanisms which limits the interaction between two modalities. In this seminar, we will focus on the integration of imaging and tabular data in Vision Models. As examples of possible applications, we will use a fetal birth weight prediction task and the estimation of mean Pulmonary Artery Pressure (mPAP). FBW prediction is a challenging task requiring clinicians to collect ultrasound videos of fetal body parts and fetal biometry measurements, while mPAP is currently measured invasively. Firstly, we will explore the feasibility of fetal birth weight prediction solely from imaging data, employing hybrid architectures that combine CNNs with Transformer-based models. Subsequently, we will focus on the refinement of predictions by incorporating the attention mechanism, computed with the assistance of tabular data. Then, we will explore the mPAP estimation with a modality mixing method based on multi-layer perceptrons. The seminar will end with an example of a different approach for tabular data utilization via the construction of interpretable scoring systems.<\/p>\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n<h2 class=\"wp-block-heading eplus-wrapper\"><strong>About the author<\/strong><\/h2>\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n<p class=\"eplus-wrapper wp-block-paragraph\">Micha\u0142 Grzeszczyk is a graduate of Computer Science studies at the Warsaw University of Technology and Technical University of Berlin (dual Master\u2019s degree). Earlier, he was a visiting researcher at The University of The West of Scotland and the Institute of Biocybernetics and Biomedical Engineering (Polish Academy of Sciences). Currently, he is a PhD student at Sano Centre for Computational Medicine in the Health Informatics Group. His research is based on multimodal data fusion with the biggest focus on combining imaging and tabular data in medicine. His work led to publications at the top-tier medical image processing conference &#8211; MICCAI. After hours, Micha\u0142 develops mobile applications, especially&nbsp;<em>Chefs\u2019 app<\/em>which is devoted to storing and sharing cooking recipes coming from multiple sources like images or websites.<\/p>\n\n\n\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n<figure class=\"wp-block-image size-large is-resized eplus-wrapper\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"536\" src=\"https:\/\/sano.science\/wp-content\/uploads\/2024\/06\/Advancing-Noninvasive-Diagnostics-Through-the-Fusion-of-Imaging-and-Clinical-Data-1024x536.jpg\" alt=\"\" class=\"wp-image-17074\" style=\"width:638px;height:auto\" srcset=\"https:\/\/sano.science\/wp-content\/uploads\/2024\/06\/Advancing-Noninvasive-Diagnostics-Through-the-Fusion-of-Imaging-and-Clinical-Data-1024x536.jpg 1024w, https:\/\/sano.science\/wp-content\/uploads\/2024\/06\/Advancing-Noninvasive-Diagnostics-Through-the-Fusion-of-Imaging-and-Clinical-Data-300x157.jpg 300w, https:\/\/sano.science\/wp-content\/uploads\/2024\/06\/Advancing-Noninvasive-Diagnostics-Through-the-Fusion-of-Imaging-and-Clinical-Data-768x402.jpg 768w, https:\/\/sano.science\/wp-content\/uploads\/2024\/06\/Advancing-Noninvasive-Diagnostics-Through-the-Fusion-of-Imaging-and-Clinical-Data.jpg 1200w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n","protected":false},"excerpt":{"rendered":"<p>Michal Grzeszczyk \u2013 PhD Student, Health Informatics, Sano Centre for Computational Medicine, Krakow, PL<\/p>\n","protected":false},"featured_media":0,"template":"","class_list":["post-15611","seminars","type-seminars","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.6 (Yoast SEO v27.6) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Advancing Noninvasive Diagnostics Through the Fusion of Imaging<\/title>\n<meta name=\"description\" content=\"Advancing Noninvasive Diagnostics Through the Fusion of Imaging and Clinical Data\" \/>\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\/advancing-noninvasive-diagnostics-through-the-fusion-of-imaging-and-clinical-data\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"137. Advancing Noninvasive Diagnostics Through the Fusion of Imaging and Clinical Data\" \/>\n<meta property=\"og:description\" content=\"Advancing Noninvasive Diagnostics Through the Fusion of Imaging and Clinical Data\" \/>\n<meta property=\"og:url\" content=\"https:\/\/sano.science\/seminars\/advancing-noninvasive-diagnostics-through-the-fusion-of-imaging-and-clinical-data\/\" \/>\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=\"2024-06-05T18:00:01+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/sano.science\/wp-content\/uploads\/2024\/06\/Advancing-Noninvasive-Diagnostics-Through-the-Fusion-of-Imaging-and-Clinical-Data-1024x536.jpg\" \/>\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=\"2 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/sano.science\\\/seminars\\\/advancing-noninvasive-diagnostics-through-the-fusion-of-imaging-and-clinical-data\\\/\",\"url\":\"https:\\\/\\\/sano.science\\\/seminars\\\/advancing-noninvasive-diagnostics-through-the-fusion-of-imaging-and-clinical-data\\\/\",\"name\":\"Advancing Noninvasive Diagnostics Through the Fusion of Imaging\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/sano.science\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/sano.science\\\/seminars\\\/advancing-noninvasive-diagnostics-through-the-fusion-of-imaging-and-clinical-data\\\/#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/sano.science\\\/seminars\\\/advancing-noninvasive-diagnostics-through-the-fusion-of-imaging-and-clinical-data\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/sano.science\\\/wp-content\\\/uploads\\\/2024\\\/06\\\/Advancing-Noninvasive-Diagnostics-Through-the-Fusion-of-Imaging-and-Clinical-Data-1024x536.jpg\",\"datePublished\":\"2024-03-04T19:40:07+00:00\",\"dateModified\":\"2024-06-05T18:00:01+00:00\",\"description\":\"Advancing Noninvasive Diagnostics Through the Fusion of Imaging and Clinical Data\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/sano.science\\\/seminars\\\/advancing-noninvasive-diagnostics-through-the-fusion-of-imaging-and-clinical-data\\\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/sano.science\\\/seminars\\\/advancing-noninvasive-diagnostics-through-the-fusion-of-imaging-and-clinical-data\\\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/sano.science\\\/seminars\\\/advancing-noninvasive-diagnostics-through-the-fusion-of-imaging-and-clinical-data\\\/#primaryimage\",\"url\":\"https:\\\/\\\/sano.science\\\/wp-content\\\/uploads\\\/2024\\\/06\\\/Advancing-Noninvasive-Diagnostics-Through-the-Fusion-of-Imaging-and-Clinical-Data.jpg\",\"contentUrl\":\"https:\\\/\\\/sano.science\\\/wp-content\\\/uploads\\\/2024\\\/06\\\/Advancing-Noninvasive-Diagnostics-Through-the-Fusion-of-Imaging-and-Clinical-Data.jpg\",\"width\":1200,\"height\":628},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/sano.science\\\/seminars\\\/advancing-noninvasive-diagnostics-through-the-fusion-of-imaging-and-clinical-data\\\/#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\":\"137. 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To enhance predictive capabilities, novel deep learning architectures are emerging, aiming to fuse information from both sources of information. Still, most solutions are based on simple concatenation or affine transformation mechanisms which limits the interaction between two modalities. In this seminar, we will focus on the integration of imaging and tabular data in Vision Models. As examples of possible applications, we will use a fetal birth weight prediction task and the estimation of mean Pulmonary Artery Pressure (mPAP). FBW prediction is a challenging task requiring clinicians to collect ultrasound videos of fetal body parts and fetal biometry measurements, while mPAP is currently measured invasively. Firstly, we will explore the feasibility of fetal birth weight prediction solely from imaging data, employing hybrid architectures that combine CNNs with Transformer-based models. Subsequently, we will focus on the refinement of predictions by incorporating the attention mechanism, computed with the assistance of tabular data. Then, we will explore the mPAP estimation with a modality mixing method based on multi-layer perceptrons. The seminar will end with an example of a different approach for tabular data utilization via the construction of interpretable scoring systems.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">Many clinical procedures involve collecting data samples in the form of imaging and tabular data. To enhance predictive capabilities, novel deep learning architectures are emerging, aiming to fuse information from both sources of information. Still, most solutions are based on simple concatenation or affine transformation mechanisms which limits the interaction between two modalities. In this seminar, we will focus on the integration of imaging and tabular data in Vision Models. As examples of possible applications, we will use a fetal birth weight prediction task and the estimation of mean Pulmonary Artery Pressure (mPAP). FBW prediction is a challenging task requiring clinicians to collect ultrasound videos of fetal body parts and fetal biometry measurements, while mPAP is currently measured invasively. Firstly, we will explore the feasibility of fetal birth weight prediction solely from imaging data, employing hybrid architectures that combine CNNs with Transformer-based models. Subsequently, we will focus on the refinement of predictions by incorporating the attention mechanism, computed with the assistance of tabular data. Then, we will explore the mPAP estimation with a modality mixing method based on multi-layer perceptrons. The seminar will end with an example of a different approach for tabular data utilization via the construction of interpretable scoring systems.<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"20px","epAnimationGeneratedClass":"edplus_anim-Ze1yfm","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/heading","attrs":{"epAnimationGeneratedClass":"edplus_anim-lcIYhp","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":"20px","epAnimationGeneratedClass":"edplus_anim-Ze1yfm","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-SCB90z","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">Micha\u0142 Grzeszczyk is a graduate of Computer Science studies at the Warsaw University of Technology and Technical University of Berlin (dual Master\u2019s degree). Earlier, he was a visiting researcher at The University of The West of Scotland and the Institute of Biocybernetics and Biomedical Engineering (Polish Academy of Sciences). Currently, he is a PhD student at Sano Centre for Computational Medicine in the Health Informatics Group. His research is based on multimodal data fusion with the biggest focus on combining imaging and tabular data in medicine. His work led to publications at the top-tier medical image processing conference - MICCAI. After hours, Micha\u0142 develops mobile applications, especially&nbsp;<em>Chefs\u2019 app<\/em>which is devoted to storing and sharing cooking recipes coming from multiple sources like images or websites.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">Micha\u0142 Grzeszczyk is a graduate of Computer Science studies at the Warsaw University of Technology and Technical University of Berlin (dual Master\u2019s degree). Earlier, he was a visiting researcher at The University of The West of Scotland and the Institute of Biocybernetics and Biomedical Engineering (Polish Academy of Sciences). Currently, he is a PhD student at Sano Centre for Computational Medicine in the Health Informatics Group. His research is based on multimodal data fusion with the biggest focus on combining imaging and tabular data in medicine. His work led to publications at the top-tier medical image processing conference - MICCAI. After hours, Micha\u0142 develops mobile applications, especially&nbsp;<em>Chefs\u2019 app<\/em>which is devoted to storing and sharing cooking recipes coming from multiple sources like images or websites.<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"30px","epAnimationGeneratedClass":"edplus_anim-Ze1yfm","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\/spacer","attrs":{"height":"20px","epAnimationGeneratedClass":"edplus_anim-Ze1yfm","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer 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