{"id":1815,"date":"2026-05-14T08:27:37","date_gmt":"2026-05-14T08:27:37","guid":{"rendered":"https:\/\/hria.utcluj.ro\/?p=1815"},"modified":"2026-05-14T08:27:38","modified_gmt":"2026-05-14T08:27:38","slug":"deep-learning-in-imagistica-medicala-segmentare-multimodala-si-diagnostic-asistat","status":"publish","type":"post","link":"https:\/\/hria.utcluj.ro\/en\/deep-learning-in-imagistica-medicala-segmentare-multimodala-si-diagnostic-asistat\/","title":{"rendered":"Deep learning \u00een imagistica medical\u0103: segmentare multimodal\u0103 \u0219i diagnostic asistat"},"content":{"rendered":"<p class=\"wp-block-paragraph\">Cercet\u0103rile recente din cadrul HRIA, realizate \u00een parteneriat cu universit\u0103\u021bi de medicin\u0103, vizeaz\u0103 \u00eembun\u0103t\u0103\u021birea preciziei diagnosticului prin arhitecturi neurale optimizate pentru diverse tipuri de scan\u0103ri (CT, RMN, X-ray, OCT).<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Segmentarea Re\u021belelor Vasculare \u0219i Analiza Radiografiilor<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Sunt introduse \u00eembun\u0103t\u0103\u021biri structurale modelelor standard pentru a cre\u0219te sensibilitatea \u00een detectarea structurilor anatomice fine:<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Angio-OCT (Re\u021bele Retiniene):<\/strong> S-a utilizat modelul <strong>I-MedSAM<\/strong>, augmentat cu un <strong>modul Frangi<\/strong>. Aceast\u0103 modificare a adus o cre\u0219tere de <strong>+1%<\/strong> pentru metricile Dice, Precision \u0219i Sensitivity \u00een segmentarea re\u021belelor vasculare.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img fetchpriority=\"high\" decoding=\"async\" width=\"456\" height=\"258\" src=\"https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/retinal-vascular-networks.png\" alt=\"\" class=\"wp-image-1816\" srcset=\"https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/retinal-vascular-networks.png 456w, https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/retinal-vascular-networks-300x170.png 300w, https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/retinal-vascular-networks-18x10.png 18w\" sizes=\"(max-width: 456px) 100vw, 456px\" \/><\/figure>\n<\/div>\n\n\n<p class=\"wp-block-paragraph\"><strong>Radiografii Toracice:<\/strong> S-a dezvoltat arhitectura <strong>YOLO_V12 \u00eembun\u0103t\u0103\u021bit\u0103<\/strong> pentru detec\u021bia patologiilor toracice (cardiomegalie, dilatarea aortei, noduli pulmonari \u0219i efuziune pleural\u0103).<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img decoding=\"async\" width=\"474\" height=\"288\" src=\"https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/pulmonary-nodules.png\" alt=\"\" class=\"wp-image-1817\" srcset=\"https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/pulmonary-nodules.png 474w, https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/pulmonary-nodules-300x182.png 300w, https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/pulmonary-nodules-18x12.png 18w\" sizes=\"(max-width: 474px) 100vw, 474px\" \/><\/figure>\n<\/div>\n\n\n<p class=\"wp-block-paragraph\"><strong>Evaluarea Scoliozei:<\/strong> Utilizarea <strong>YOLO OBB<\/strong> (Oriented Bounding Boxes) combinat cu un model I-MedSAM modificat pentru calculul automat al unghiului Cobb.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img decoding=\"async\" width=\"512\" height=\"262\" src=\"https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/cobb-angle.png\" alt=\"\" class=\"wp-image-1818\" srcset=\"https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/cobb-angle.png 512w, https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/cobb-angle-300x154.png 300w, https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/cobb-angle-18x9.png 18w\" sizes=\"(max-width: 512px) 100vw, 512px\" \/><\/figure>\n<\/div>\n\n\n<h3 class=\"wp-block-heading\"><strong>Recunoa\u0219terea Tumorilor prin Domain Adaptation Learning (DAL)<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">O contribu\u021bie major\u0103 const\u0103 \u00een utilizarea tehnicilor de \u00eenv\u0103\u021bare prin adaptarea domeniului pentru recunoa\u0219terea tumorilor de ficat \u0219i pancreas:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Dataset:<\/strong> Imagini CT cu contrast de la pacien\u021bi cu tumori hepatice (45 pacien\u021bi) \u0219i pancreatice (30 pacien\u021bi).<\/li>\n\n\n\n<li><strong>Metodologie:<\/strong> S-a utilizat un set de date mixt, urmat de fine-tuning prin <strong>DAL<\/strong>.<\/li>\n\n\n\n<li><strong>Performan\u021b\u0103:<\/strong> Modele precum <strong>ResNet50, ResNet101, InceptionV3 \u0219i EfficientNetB0<\/strong> au fost evaluate comparativ. Rezultatele indic\u0103 faptul c\u0103 abordarea DAL (Acc_DAL) dep\u0103\u0219e\u0219te constant acurate\u021bea modelelor antrenate standard sau pe seturi mixte.<\/li>\n<\/ul>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-8f761849 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\"><div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"521\" height=\"321\" src=\"https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/liver-tumors.png\" alt=\"\" class=\"wp-image-1819\" srcset=\"https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/liver-tumors.png 521w, https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/liver-tumors-300x185.png 300w, https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/liver-tumors-18x12.png 18w\" sizes=\"(max-width: 521px) 100vw, 521px\" \/><figcaption class=\"wp-element-caption\">Tumori hepatice &#8211; acurate\u021be maxim\u0103 89.2%<\/figcaption><\/figure>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\"><div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"592\" height=\"341\" src=\"https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/pancreatic-tumors.png\" alt=\"\" class=\"wp-image-1820\" srcset=\"https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/pancreatic-tumors.png 592w, https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/pancreatic-tumors-300x173.png 300w, https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/pancreatic-tumors-18x10.png 18w\" sizes=\"(max-width: 592px) 100vw, 592px\" \/><figcaption class=\"wp-element-caption\">Tumori pancreatice &#8211; acurate\u021be maxim\u0103 98.9%<\/figcaption><\/figure>\n<\/div><\/div>\n<\/div>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Direc\u021bii de Cercetare \u0219i Diagnostic Asistat (Parteneriat UMF)<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">\u00cen colaborare cu UMF Cluj, sunt \u00een curs de dezvoltare sisteme de diagnostic asistat de AI pentru patologii hepatice complexe:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Leziuni Focale:<\/strong> Clasificarea leziunilor hepatice utiliz\u00e2nd protocoale RMN abreviate.<\/li>\n\n\n\n<li><strong>Fibroz\u0103 Hepatic\u0103:<\/strong> Clasificarea gradului de fibroz\u0103 prin analiz\u0103 multimodal\u0103 (CT\/RMN\/Ultrasunet).<\/li>\n\n\n\n<li><strong>Monitorizarea Tratamentului:<\/strong> Evaluarea r\u0103spunsului la tratament pentru leziunile hepatice prin scan\u0103ri CT.<\/li>\n\n\n\n<li><strong>Hipertensiune Portal\u0103:<\/strong> Diagnosticul automatizat al hipertensiunii portale utiliz\u00e2nd date CT.<\/li>\n<\/ul>","protected":false},"excerpt":{"rendered":"<p>Cercet\u0103rile recente din cadrul HRIA, realizate \u00een parteneriat cu universit\u0103\u021bi de medicin\u0103, vizeaz\u0103 \u00eembun\u0103t\u0103\u021birea preciziei diagnosticului prin arhitecturi neurale optimizate pentru diverse tipuri de scan\u0103ri (CT, RMN, X-ray, OCT). Segmentarea Re\u021belelor Vasculare \u0219i Analiza Radiografiilor Sunt introduse \u00eembun\u0103t\u0103\u021biri structurale modelelor standard pentru a cre\u0219te sensibilitatea \u00een detectarea structurilor anatomice fine: Angio-OCT (Re\u021bele Retiniene): S-a utilizat&hellip;&nbsp;<\/p>","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"neve_meta_sidebar":"","neve_meta_container":"default","neve_meta_enable_content_width":"on","neve_meta_content_width":100,"neve_meta_title_alignment":"","neve_meta_author_avatar":"off","neve_post_elements_order":"[\"content\"]","neve_meta_disable_header":"off","neve_meta_disable_footer":"","neve_meta_disable_title":"","_themeisle_gutenberg_block_has_review":false,"footnotes":""},"categories":[7],"tags":[],"class_list":["post-1815","post","type-post","status-publish","format-standard","hentry","category-rezultate"],"_links":{"self":[{"href":"https:\/\/hria.utcluj.ro\/en\/wp-json\/wp\/v2\/posts\/1815","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hria.utcluj.ro\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hria.utcluj.ro\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hria.utcluj.ro\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/hria.utcluj.ro\/en\/wp-json\/wp\/v2\/comments?post=1815"}],"version-history":[{"count":1,"href":"https:\/\/hria.utcluj.ro\/en\/wp-json\/wp\/v2\/posts\/1815\/revisions"}],"predecessor-version":[{"id":1821,"href":"https:\/\/hria.utcluj.ro\/en\/wp-json\/wp\/v2\/posts\/1815\/revisions\/1821"}],"wp:attachment":[{"href":"https:\/\/hria.utcluj.ro\/en\/wp-json\/wp\/v2\/media?parent=1815"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hria.utcluj.ro\/en\/wp-json\/wp\/v2\/categories?post=1815"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hria.utcluj.ro\/en\/wp-json\/wp\/v2\/tags?post=1815"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}