# Large-scale biometry with interpretable neural network regression on UK Biobank body MRI

arXiv: https://arxiv.org/abs/2002.06862 · HTML: https://mlsanity.com/paper/2002.06862/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Feb 2020.

## Built on by (most cited first, 7 of 7)

- [How Reliable is Your Regression Model's Uncertainty Under Real-World Distribution Shifts?](https://mlsanity.com/paper/2302.03679/) (2023)
- [Interpretability of Machine Learning: Recent Advances and Future Prospects](https://mlsanity.com/paper/2305.00537/) (2023)
- [Uncertainty-Aware Body Composition Analysis with Deep Regression Ensembles on UK Biobank MRI](https://mlsanity.com/paper/2101.06963/) (2021)
- [Deep regression for uncertainty-aware and interpretable analysis of large-scale body MRI](https://mlsanity.com/paper/2105.07797/) (2021)
- [MIMIR: Deep Regression for Automated Analysis of UK Biobank Body MRI](https://mlsanity.com/paper/2106.11731/) (2021)
- [Swap-Free Fat-Water Separation in Dixon MRI using Conditional Generative Adversarial Networks](https://mlsanity.com/paper/2107.14175/) (2021)
- [Machine Learning Techniques for MRI Data Processing at Expanding Scale](https://mlsanity.com/paper/2404.14326/) (2024)

## Abstract (arXiv)

In a large-scale medical examination, the UK Biobank study has successfully imaged more than 32,000 volunteer participants with magnetic resonance imaging (MRI). Each scan is linked to extensive metadata, providing a comprehensive medical survey of imaged anatomy and related health states. Despite its potential for research, this vast amount of data presents a challenge to established methods of evaluation, which often rely on manual input. To date, the range of reference values for cardiovascular and metabolic risk factors is therefore incomplete. In this work, neural networks were trained for image-based regression to infer various biological metrics from the neck-to-knee body MRI automatically. The approach requires no manual intervention or direct access to reference segmentations for training. The examined fields span 64 variables derived from anthropometric measurements, dual-energy X-ray absorptiometry (DXA), atlas-based segmentations, and dedicated liver scans. With the ResNet50, the standardized framework achieves a close fit to the target values (median R^2 > 0.97) in cross-validation. Interpretation of aggregated saliency maps suggests that the network correctly targets specific body regions and limbs, and learned to emulate different modalities. On several body composition metrics, the quality of the predictions is within the range of variability observed between established gold standard techniques.