{"id":3013,"date":"2025-10-11T23:42:08","date_gmt":"2025-10-11T21:42:08","guid":{"rendered":"https:\/\/portale2.unime.it\/ai-healthlab\/?p=3013"},"modified":"2025-10-15T12:12:53","modified_gmt":"2025-10-15T10:12:53","slug":"re-identification-of-brain-mris","status":"publish","type":"post","link":"https:\/\/portale2.unime.it\/ai-healthlab\/en\/re-identification-of-brain-mris\/","title":{"rendered":"Re-Identification of Brain MRIs"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"3013\" class=\"elementor elementor-3013\">\n\t\t\t\t<div class=\"elementor-element elementor-element-584050f0 e-flex e-con-boxed e-con e-parent\" data-id=\"584050f0\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-3aec96c3 elementor-widget elementor-widget-text-editor\" data-id=\"3aec96c3\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>\nDeepBrainPrint is a contrastive deep learning framework for re-identification of subjects through brain MRIs, accepted at the <b>Medical Imaging with Deep Learning (MIDL) 2023<\/b> conference. Within the landscape of brain fingerprinting, DeepBrainPrint represents the first semi-auto-supervised approach, capable of extracting a \u201cdigital fingerprint\u201d from brain morphology, enabling the effective retrieval of all scans related to the same subject from large medical imaging datasets.\n<\/p>\n<p><b>Motivations &amp; Objectives<\/b><\/p>\n<ul>\n<li>Enable automatic patient re-identification (&#8220;brain re-identification&#8221;) within increasingly large brain MRI databases, simplifying longitudinal management of clinical acquisitions.<\/li>\n<li>Improve identification robustness, overcoming the limitations of previous methods when facing changes due to aging, disease progression, differences in scanner and acquisition sequence.<\/li>\n<li>Lay the groundwork for new systems of morphological retrieval, also useful for finding similar patients in clinical and comparative research settings.<\/li>\n<\/ul>\n<p><b>Methods<\/b><\/p>\n<ul>\n<li>Semi-auto-supervised contrastive pipeline, combining self-supervised and weak supervision learning to create robust numerical (fingerprint) representations of brain morphology.<\/li>\n<li>Main innovations:\n<ul>\n<li>Adaptive loss function to better guide model convergence.<\/li>\n<li>New image transformations to boost robustness against intensity differences (contrasts), aging, and pathology.<\/li>\n<\/ul>\n<\/li>\n<li>Extensive tests on T1-weighted MRI (ADNI) and synthetic multimodal datasets.<\/li>\n<\/ul>\n<p><b>Results &amp; Application<\/b><\/p>\n<ul>\n<li>DeepBrainPrint outperforms cutting-edge deep metric learning techniques (InfoNCE, SoftTriple, SimCLR, BarlowTwins) in accuracy, computational efficiency, and ability to generalize to different subjects.<\/li>\n<li>The model is suitable not only for re-identification but also for automatic search of scans with similar morphological features (e.g., shape, lesions, atrophies), which can be useful even between different patients.<\/li>\n<li>Scalable solution, ready for adoption in large biobanks and healthcare networks.<\/li>\n<\/ul>\n<h3><br><\/h3><h3>Related Scientific Articles<\/h3>\n<ul>\n<li>\n    DeepBrainPrint: A Novel Contrastive Framework for Brain MRI Re-Identification<br>\n    <a href=\"https:\/\/arxiv.org\/abs\/2302.13057\">arXiv preprint arXiv:2302.13057 (2023)<\/a> \u2013<br>\n    <a href=\"https:\/\/proceedings.mlr.press\/v227\/puglisi24a.html\">MIDL Proceedings<\/a><br>\n    <br>    <b>Authors:<\/b> Lemuel Puglisi, Frederik Barkhof, Daniel C. Alexander, Geoffrey JM Parker, Arman Eshaghi, Daniele Rav\u00ec\n  <\/li>\n<\/ul>\n<h3><br><\/h3><h3>Team &amp; Authors<\/h3>\n<ul>\n<li><a href=\"https:\/\/github.com\/LemuelPuglisi\">Lemuel Puglisi<\/a> (First author, methodology development)<\/li>\n<li><a href=\"https:\/\/github.com\/daniravi\">Daniele Rav\u00ec<\/a> (Co-author, scientific supervision)<\/li>\n<li>Frederik Barkhof, Daniel C. Alexander, Geoffrey JM Parker, Arman Eshaghi (Co-authors, supervision, data support)<\/li>\n<\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>DeepBrainPrint is a contrastive deep learning framework for re-identification of subjects through brain MRIs, accepted at the Medical Imaging with Deep Learning (MIDL) 2023 conference. Within the landscape of brain fingerprinting, DeepBrainPrint represents the first semi-auto-supervised approach, capable of extracting a \u201cdigital fingerprint\u201d from brain morphology, enabling the effective retrieval of all scans related to&hellip; <a class=\"more-link\" href=\"https:\/\/portale2.unime.it\/ai-healthlab\/en\/re-identification-of-brain-mris\/\">Continua a leggere <span class=\"screen-reader-text\">Re-Identification of Brain MRIs<\/span><\/a><\/p>\n","protected":false},"author":94,"featured_media":2335,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[34],"tags":[],"class_list":["post-3013","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news-eng-en","entry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Re-Identification of Brain MRIs - AI-HealthLab<\/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:\/\/portale2.unime.it\/ai-healthlab\/en\/re-identification-of-brain-mris\/\" \/>\n<meta property=\"og:locale\" content=\"it_IT\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Re-Identification of Brain MRIs - AI-HealthLab\" \/>\n<meta property=\"og:description\" content=\"DeepBrainPrint is a contrastive deep learning framework for re-identification of subjects through brain MRIs, accepted at the Medical Imaging with Deep Learning (MIDL) 2023 conference. Within the landscape of brain fingerprinting, DeepBrainPrint represents the first semi-auto-supervised approach, capable of extracting a \u201cdigital fingerprint\u201d from brain morphology, enabling the effective retrieval of all scans related to&hellip; Continua a leggere Re-Identification of Brain MRIs\" \/>\n<meta property=\"og:url\" content=\"https:\/\/portale2.unime.it\/ai-healthlab\/en\/re-identification-of-brain-mris\/\" \/>\n<meta property=\"og:site_name\" content=\"AI-HealthLab\" \/>\n<meta property=\"article:published_time\" content=\"2025-10-11T21:42:08+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-10-15T10:12:53+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/portale2.unime.it\/ai-healthlab\/wp-content\/uploads\/sites\/94\/2022\/03\/image_edited-e1760275548120.png\" \/>\n\t<meta property=\"og:image:width\" content=\"610\" \/>\n\t<meta property=\"og:image:height\" content=\"413\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"dravi\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Scritto da\" \/>\n\t<meta name=\"twitter:data1\" content=\"dravi\" \/>\n\t<meta name=\"twitter:label2\" content=\"Tempo di lettura stimato\" \/>\n\t<meta name=\"twitter:data2\" content=\"2 minuti\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/portale2.unime.it\\\/ai-healthlab\\\/en\\\/re-identification-of-brain-mris\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/portale2.unime.it\\\/ai-healthlab\\\/en\\\/re-identification-of-brain-mris\\\/\"},\"author\":{\"name\":\"dravi\",\"@id\":\"https:\\\/\\\/portale2.unime.it\\\/ai-healthlab\\\/#\\\/schema\\\/person\\\/d0bae234160fa13008ca10037ea1f672\"},\"headline\":\"Re-Identification of Brain MRIs\",\"datePublished\":\"2025-10-11T21:42:08+00:00\",\"dateModified\":\"2025-10-15T10:12:53+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/portale2.unime.it\\\/ai-healthlab\\\/en\\\/re-identification-of-brain-mris\\\/\"},\"wordCount\":312,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\\\/\\\/portale2.unime.it\\\/ai-healthlab\\\/#organization\"},\"image\":{\"@id\":\"https:\\\/\\\/portale2.unime.it\\\/ai-healthlab\\\/en\\\/re-identification-of-brain-mris\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/portale2.unime.it\\\/ai-healthlab\\\/wp-content\\\/uploads\\\/sites\\\/94\\\/2022\\\/03\\\/image_edited-e1760275548120.png\",\"articleSection\":[\"NEWS eng\"],\"inLanguage\":\"it-IT\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\\\/\\\/portale2.unime.it\\\/ai-healthlab\\\/en\\\/re-identification-of-brain-mris\\\/#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/portale2.unime.it\\\/ai-healthlab\\\/en\\\/re-identification-of-brain-mris\\\/\",\"url\":\"https:\\\/\\\/portale2.unime.it\\\/ai-healthlab\\\/en\\\/re-identification-of-brain-mris\\\/\",\"name\":\"Re-Identification of Brain MRIs - 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