# Deep regression for uncertainty-aware and interpretable analysis of large-scale body MRI

arXiv: https://arxiv.org/abs/2105.07797 · HTML: https://mlsanity.com/paper/2105.07797/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 29 references that are in the map.
- First posted on arXiv 17 May 2021.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [Large-scale biometry with interpretable neural network regression on UK Biobank body MRI](https://mlsanity.com/paper/2002.06862/) (2020)
- [Direct Multitype Cardiac Indices Estimation via Joint Representation and Regression Learning](https://mlsanity.com/paper/1705.09307/) (2017)
- [Kidney segmentation in neck-to-knee body MRI of 40,000 UK Biobank participants](https://mlsanity.com/paper/2006.06996/) (2020)
- [Fast Graph-Cut Based Optimization for Practical Dense Deformable Registration of Volume Images](https://mlsanity.com/paper/1810.08427/) (2018)
- [Large-Scale Analysis of Iliopsoas Muscle Volumes in the UK Biobank](https://mlsanity.com/paper/2008.05217/) (2020)
- [Uncertainty-Aware Body Composition Analysis with Deep Regression Ensembles on UK Biobank MRI](https://mlsanity.com/paper/2101.06963/) (2021)
- [Large-scale inference of liver fat with neural networks on UK Biobank body MRI](https://mlsanity.com/paper/2006.16777/) (2020)

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

- [MIMIR: Deep Regression for Automated Analysis of UK Biobank Body MRI](https://mlsanity.com/paper/2106.11731/) (2021)

## Abstract (arXiv)

Large-scale medical studies such as the UK Biobank examine thousands of volunteer participants with medical imaging techniques. Combined with the vast amount of collected metadata, anatomical information from these images has the potential for medical analyses at unprecedented scale. However, their evaluation often requires manual input and long processing times, limiting the amount of reference values for biomarkers and other measurements available for research. Recent approaches with convolutional neural networks for regression can perform these evaluations automatically. On magnetic resonance imaging (MRI) data of more than 40,000 UK Biobank subjects, these systems can estimate human age, body composition and more. This style of analysis is almost entirely data-driven and no manual intervention or guidance with manually segmented ground truth images is required. The networks often closely emulate the reference method that provided their training data and can reach levels of agreement comparable to the expected variability between established medical gold standard techniques. The risk of silent failure can be individually quantified by predictive uncertainty obtained from a mean-variance criterion and ensembling. Saliency analysis furthermore enables an interpretation of the underlying relevant image features and showed that the networks learned to correctly target specific organs, limbs, and regions of interest.