# Bayesian Neural Networks for Uncertainty Estimation of Imaging Biomarkers

arXiv: https://arxiv.org/abs/2008.12680 · HTML: https://mlsanity.com/paper/2008.12680/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2020.

## Built on by (most cited first, 4 of 4)

- [A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methods](https://mlsanity.com/paper/2310.06873/) (2023)
- [From Pointwise to Powerhouse: Initialising Neural Networks with Generative Models](https://mlsanity.com/paper/2310.16695/) (2023)
- [Uncertainty Quantified Deep Learning and Regression Analysis Framework for Image Segmentation of Skin Cancer Lesions](https://mlsanity.com/paper/2412.20007/) (2024)
- [PF-DAformer: Proximal Femur Segmentation via Domain Adaptive Transformer for Dual-Center QCT](https://mlsanity.com/paper/2510.26903/) (2025)

## Abstract (arXiv)

Image segmentation enables to extract quantitative measures from scans that can serve as imaging biomarkers for diseases. However, segmentation quality can vary substantially across scans, and therefore yield unfaithful estimates in the follow-up statistical analysis of biomarkers. The core problem is that segmentation and biomarker analysis are performed independently. We propose to propagate segmentation uncertainty to the statistical analysis to account for variations in segmentation confidence. To this end, we evaluate four Bayesian neural networks to sample from the posterior distribution and estimate the uncertainty. We then assign confidence measures to the biomarker and propose statistical models for its integration in group analysis and disease classification. Our results for segmenting the liver in patients with diabetes mellitus clearly demonstrate the improvement of integrating biomarker uncertainty in the statistical inference.