{"id":317,"date":"2025-06-18T07:47:36","date_gmt":"2025-06-18T07:47:36","guid":{"rendered":"https:\/\/www.inf.u-szeged.hu\/~ssip\/2025\/?page_id=317"},"modified":"2025-06-18T07:49:59","modified_gmt":"2025-06-18T07:49:59","slug":"krisztian-koos","status":"publish","type":"page","link":"https:\/\/www.inf.u-szeged.hu\/~ssip\/2025\/krisztian-koos\/","title":{"rendered":"Kriszti\u00e1n Ko\u00f3s"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"317\" class=\"elementor elementor-317\">\n\t\t\t\t<div class=\"elementor-element elementor-element-1dd85b9 e-flex e-con-boxed e-con e-parent\" data-id=\"1dd85b9\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-931dcf9 elementor-widget elementor-widget-text-editor\" data-id=\"931dcf9\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><strong>Name<\/strong>:&nbsp;Kriszti\u00e1n Ko\u00f3s<br><strong>Affiliation<\/strong>:GE Healthcare<br><strong>Primary research interest<\/strong>:&nbsp;medical image processing, self-supervised learning<\/p>\n<p><strong>Title of the lecture<\/strong>: Multi Anatomy X-ray Foundation Model<br><strong>Keywords<\/strong>:&nbsp;self-supervised learning, radiology, x-ray, foundation models<br><strong>Summary<\/strong>:&nbsp;<span style=\"font-size: 1rem;\">Self-supervised\n learning (SSL) has emerged as a powerful approach for developing \ngeneral-purpose models that perform exceptionally well across various \ndownstream tasks, including\n classification and segmentation. Among these, DINOv2 stands out as one \nof the most prominent methods. In the medical domain\u2014particularly in \nX-ray imaging\u2014foundation models are gaining significant traction. \nChest-specific models such as RadDINO and RayDINO\n demonstrate the effectiveness of SSL using imaging data alone.&nbsp;<\/span><span style=\"font-size: 1rem;\">In\n this talk, a novel multi-anatomy X-ray model pretrained using \nself-supervised learning will be presented. The model is evaluated on a \ndiverse set of tasks, including image-to-image\n retrieval, anatomical localization, report generation, and more, \nshowcasing its versatility and generalization capabilities.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\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>Name:&nbsp;Kriszti\u00e1n Ko\u00f3sAffiliation:GE HealthcarePrimary research interest:&nbsp;medical image processing, self-supervised learning Title of the lecture: Multi Anatomy X-ray Foundation ModelKeywords:&nbsp;self-supervised learning, radiology, x-ray, foundation modelsSummary:&nbsp;Self-supervised learning (SSL) has emerged as a powerful approach for developing general-purpose models that perform exceptionally well across various downstream tasks, including classification and segmentation. Among these, DINOv2 stands out as one of &hellip; <\/p>\n<p class=\"link-more\"><a href=\"https:\/\/www.inf.u-szeged.hu\/~ssip\/2025\/krisztian-koos\/\" class=\"more-link\">Continue reading<span class=\"screen-reader-text\"> &#8220;Kriszti\u00e1n Ko\u00f3s&#8221;<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-317","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/www.inf.u-szeged.hu\/~ssip\/2025\/wp-json\/wp\/v2\/pages\/317","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.inf.u-szeged.hu\/~ssip\/2025\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.inf.u-szeged.hu\/~ssip\/2025\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.inf.u-szeged.hu\/~ssip\/2025\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.inf.u-szeged.hu\/~ssip\/2025\/wp-json\/wp\/v2\/comments?post=317"}],"version-history":[{"count":3,"href":"https:\/\/www.inf.u-szeged.hu\/~ssip\/2025\/wp-json\/wp\/v2\/pages\/317\/revisions"}],"predecessor-version":[{"id":320,"href":"https:\/\/www.inf.u-szeged.hu\/~ssip\/2025\/wp-json\/wp\/v2\/pages\/317\/revisions\/320"}],"wp:attachment":[{"href":"https:\/\/www.inf.u-szeged.hu\/~ssip\/2025\/wp-json\/wp\/v2\/media?parent=317"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}