{"id":12736,"date":"2026-10-09T13:25:18","date_gmt":"2026-10-09T11:25:18","guid":{"rendered":"https:\/\/vicorob.udg.edu\/?p=12736"},"modified":"2026-10-09T13:28:37","modified_gmt":"2026-10-09T11:28:37","slug":"doctoral-thesis-advances-in-generative-modeling-for-medical-data-applications-in-imaging-video-and-multimodal-synthesis","status":"publish","type":"post","link":"https:\/\/vicorob.udg.edu\/ca\/doctoral-thesis-advances-in-generative-modeling-for-medical-data-applications-in-imaging-video-and-multimodal-synthesis\/","title":{"rendered":"Doctoral Thesis: Advances in Generative Modeling for Medical Data: Applications in Imaging, Video, and Multimodal Synthesis."},"content":{"rendered":"

\"\"<\/p>\n

By: Ricardo Montoya del Angel<\/strong>
\nSupervised by:\u00a0 Dr. Robert Mart\u00ed Marly<\/strong><\/p>\n

 <\/p>\n

Thesis Overview<\/h3>\n

Artificial intelligence (AI)<\/strong> holds high potential for automated medical diagnosis<\/strong>, but relies heavily on vast, well-annotated datasets. In specialized fields like Contrast-Enhanced Mammography (CEM)<\/strong> or point-of-care echocardiography<\/strong>, data scarcity remains a critical bottleneck for clinical deployment. This thesis investigates probabilistic generative modeling strategies<\/strong> to synthesize realistic, clinically valid medical data across three levels of complexity:<\/p>\n

    \n
  1. Physically-Informed Uncertainty:<\/strong> Maps acquisition and annotation uncertainties into bounded generative variance, enhancing radiomic biomarker prediction<\/strong> in small CEM cohorts.<\/li>\n
  2. Controllable Mammography Synthesis:<\/strong> Leverages latent diffusion models<\/strong> for full-field synthesis and localized lesion inpainting, transferring knowledge to data-scarce CEM.<\/li>\n
  3. Efficient Video Synthesis:<\/strong> Combines 1D tokenization and masked transformers<\/strong> to reconstruct missing echocardiographic views (2-chamber from 4-chamber), enabling Ejection Fraction estimation<\/strong> on portable ultrasound devices.<\/li>\n<\/ol>\n

     <\/p>\n

    The findings support the idea that synthetic data generated with physical and clinical rigor<\/strong> effectively overcomes clinical data constraints, enhancing diagnostic accuracy and decision support<\/strong> in clinical practice.<\/p>\n

     <\/p>\n

    \"\"<\/p>\n

    Photo: Illustration of the research developed in the doctoral thesis on generative modeling for medical data synthesis.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

    By: Ricardo Montoya del Angel Supervised by:\u00a0 Dr. Robert Mart\u00ed Marly   Thesis Overview Artificial intelligence (AI) holds high potential for automated medical diagnosis, but relies heavily on vast, well-annotated datasets. In specialized fields like Contrast-Enhanced Mammography (CEM) or point-of-care echocardiography, data scarcity remains a critical bottleneck for clinical deployment. This thesis investigates probabilistic generative… <\/p>\n","protected":false},"author":2,"featured_media":12756,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"neve_meta_sidebar":"","neve_meta_container":"","neve_meta_enable_content_width":"off","neve_meta_content_width":70,"neve_meta_title_alignment":"","neve_meta_author_avatar":"","neve_post_elements_order":"","neve_meta_disable_header":"","neve_meta_disable_footer":"","neve_meta_disable_title":"","footnotes":""},"categories":[58],"tags":[],"class_list":["post-12736","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-scientific-results"],"acf":[],"yoast_head":"\nDoctoral Thesis: Advances in Generative Modeling for Medical Data: Applications in Imaging, Video, and Multimodal Synthesis. - Vicorob<\/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:\/\/vicorob.udg.edu\/doctoral-thesis-advances-in-generative-modeling-for-medical-data-applications-in-imaging-video-and-multimodal-synthesis\/\" \/>\n<meta property=\"og:locale\" content=\"ca_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Doctoral Thesis: Advances in Generative Modeling for Medical Data: Applications in Imaging, Video, and Multimodal Synthesis. - Vicorob\" \/>\n<meta property=\"og:description\" content=\"By: Ricardo Montoya del Angel Supervised by:\u00a0 Dr. Robert Mart\u00ed Marly   Thesis Overview Artificial intelligence (AI) holds high potential for automated medical diagnosis, but relies heavily on vast, well-annotated datasets. 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