{"ID":2886870,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.02307","arxiv_id":"2508.02307","title":"Whole-body Representation Learning For Competing Preclinical Disease Risk Assessment","abstract":"Reliable preclinical disease risk assessment is essential to move public healthcare from reactive treatment to proactive identification and prevention. However, image-based risk prediction algorithms often consider one condition at a time and depend on hand-crafted features obtained through segmentation tools. We propose a whole-body self-supervised representation learning method for the preclinical disease risk assessment under a competing risk modeling. This approach outperforms whole-body radiomics in multiple diseases, including cardiovascular disease (CVD), type 2 diabetes (T2D), chronic obstructive pulmonary disease (COPD), and chronic kidney disease (CKD). Simulating a preclinical screening scenario and subsequently combining with cardiac MRI, it sharpens further the prediction for CVD subgroups: ischemic heart disease (IHD), hypertensive diseases (HD), and stroke. The results indicate the translational potential of whole-body representations as a standalone screening modality and as part of a multi-modal framework within clinical workflows for early personalized risk stratification. The code is available at https://github.com/yayapa/WBRLforCR/","short_abstract":"Reliable preclinical disease risk assessment is essential to move public healthcare from reactive treatment to proactive identification and prevention. However, image-based risk prediction algorithms often consider one condition at a time and depend on hand-crafted features obtained through segmentation tools. We propo...","url_abs":"https://arxiv.org/abs/2508.02307","url_pdf":"https://arxiv.org/pdf/2508.02307v1","authors":"[\"Dmitrii Seletkov\",\"Sophie Starck\",\"Ayhan Can Erdur\",\"Yundi Zhang\",\"Daniel Rueckert\",\"Rickmer Braren\"]","published":"2025-08-04T11:20:31Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.LG\"]","methods":"[]","has_code":false,"code_links":[{"ID":611374,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2886870,"paper_url":"https://arxiv.org/abs/2508.02307","paper_title":"Whole-body Representation Learning For Competing Preclinical Disease Risk Assessment","repo_url":"https://github.com/yayapa/WBRLforCR","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}