# Uncertainty Estimation in Medical Image Denoising with Bayesian Deep Image Prior

arXiv: https://arxiv.org/abs/2008.08837 · HTML: https://mlsanity.com/paper/2008.08837/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Aug 2020.

## Built on by (most cited first, 18 of 18)

- [MR Image Denoising and Super-Resolution Using Regularized Reverse Diffusion](https://mlsanity.com/paper/2203.12621/) (2022)
- [A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methods](https://mlsanity.com/paper/2310.06873/) (2023)
- [UnCRtainTS: Uncertainty Quantification for Cloud Removal in Optical Satellite Time Series](https://mlsanity.com/paper/2304.05464/) (2023)
- [Sampling-based inference for large linear models, with application to linearised Laplace](https://mlsanity.com/paper/2210.04994/) (2022)
- [Uncertainty Estimation for Computed Tomography with a Linearised Deep Image Prior](https://mlsanity.com/paper/2203.00479/) (2022)
- [Equilibrated Zeroth-Order Unrolled Deep Networks for Accelerated MRI](https://mlsanity.com/paper/2112.09891/) (2021)
- [Self-Supervised Pre-Training for Deep Image Prior-Based Robust PET Image Denoising](https://mlsanity.com/paper/2302.13546/) (2023)
- [Cycle Consistency-based Uncertainty Quantification of Neural Networks in Inverse Imaging Problems](https://mlsanity.com/paper/2305.12852/) (2023)
- [Posterior Temperature Optimization in Variational Inference for Inverse Problems](https://mlsanity.com/paper/2106.07533/) (2021)
- [Posterior temperature optimized Bayesian models for inverse problems in medical imaging](https://mlsanity.com/paper/2202.00986/) (2022)
- [Content-Aware Differential Privacy with Conditional Invertible Neural Networks](https://mlsanity.com/paper/2207.14625/) (2022)
- [Targeted Gradient Descent: A Novel Method for Convolutional Neural Networks Fine-tuning and Online-learning](https://mlsanity.com/paper/2109.14729/) (2021)
- [Uncertainty Estimation and Out-of-Distribution Detection for Deep Learning-Based Image Reconstruction using the Local Lipschitz](https://mlsanity.com/paper/2305.07618/) (2023)
- [TransMorph: Transformer for unsupervised medical image registration](https://mlsanity.com/paper/2111.10480/) (2021)
- [One-Dimensional Deep Image Prior for Curve Fitting of S-Parameters from Electromagnetic Solvers](https://mlsanity.com/paper/2306.04001/) (2023)
- [Uncertainty-Driven Multi-Scale Feature Fusion Network for Real-time Image Deraining](https://mlsanity.com/paper/2307.09728/) (2023)
- [Uncertainty quantification for White Matter Hyperintensity segmentation detects silent failures and improves automated Fazekas quantification](https://mlsanity.com/paper/2411.17571/) (2024)
- [On Hallucinations in Inverse Problems: Fundamental Limits and Provable Assessment Methods](https://mlsanity.com/paper/2605.13146/) (2026)

## Abstract (arXiv)

Uncertainty quantification in inverse medical imaging tasks with deep learning has received little attention. However, deep models trained on large data sets tend to hallucinate and create artifacts in the reconstructed output that are not anatomically present. We use a randomly initialized convolutional network as parameterization of the reconstructed image and perform gradient descent to match the observation, which is known as deep image prior. In this case, the reconstruction does not suffer from hallucinations as no prior training is performed. We extend this to a Bayesian approach with Monte Carlo dropout to quantify both aleatoric and epistemic uncertainty. The presented method is evaluated on the task of denoising different medical imaging modalities. The experimental results show that our approach yields well-calibrated uncertainty. That is, the predictive uncertainty correlates with the predictive error. This allows for reliable uncertainty estimates and can tackle the problem of hallucinations and artifacts in inverse medical imaging tasks.