# Whole-body Representation Learning For Competing Preclinical Disease Risk Assessment

arXiv: https://arxiv.org/abs/2508.02307 · HTML: https://mlsanity.com/paper/2508.02307/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 31 references that are in the map.
- First posted on arXiv 4 Aug 2025.

## Builds on (most cited first, 9 of 9)

- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Deep Learning for Survival Analysis: A Review](https://mlsanity.com/paper/2305.14961/) (2023)
- [Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data with Competing Risks](https://mlsanity.com/paper/2003.01176/) (2020)
- [Neural Fine-Gray: Monotonic neural networks for competing risks](https://mlsanity.com/paper/2305.06703/) (2023)
- [Learning Interpretable Anatomical Features Through Deep Generative Models: Application to Cardiac Remodeling](https://mlsanity.com/paper/1807.06843/) (2018)
- [Image-based Survival Analysis for Lung Cancer Patients using CNNs](https://mlsanity.com/paper/1808.09679/) (2018)
- [VIBESegmentator: Full Body MRI Segmentation for the NAKO and UK Biobank](https://mlsanity.com/paper/2406.00125/) (2024)
- [Whole Heart 3D+T Representation Learning Through Sparse 2D Cardiac MR Images](https://mlsanity.com/paper/2406.00329/) (2024)
- [Development of an accessible 10-year Digital CArdioVAscular (DiCAVA) risk assessment: a UK Biobank study](https://mlsanity.com/paper/2104.10079/) (2021)

## Built on by (most cited first, 1 of 1)

- [Survival In-Context: Amortized Bayesian Survival Analysis via Prior-Fitted Networks](https://mlsanity.com/paper/2603.29475/) (2026)

## Abstract (arXiv)

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/