# Leveraging Deep Stein's Unbiased Risk Estimator for Unsupervised X-ray Denoising

arXiv: https://arxiv.org/abs/1811.12488 · HTML: https://mlsanity.com/paper/1811.12488/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2018.

## Built on by (most cited first, 2 of 2)

- [R2C-GAN: Restore-to-Classify Generative Adversarial Networks for Blind X-Ray Restoration and COVID-19 Classification](https://mlsanity.com/paper/2209.14770/) (2022)
- [Deep Learning Meets Adaptive Filtering: A Stein's Unbiased Risk Estimator Approach](https://mlsanity.com/paper/2307.16708/) (2023)

## Abstract (arXiv)

Among the plethora of techniques devised to curb the prevalence of noise in medical images, deep learning based approaches have shown the most promise. However, one critical limitation of these deep learning based denoisers is the requirement of high-quality noiseless ground truth images that are difficult to obtain in many medical imaging applications such as X-rays. To circumvent this issue, we leverage recently proposed approach of [7] that incorporates Stein's Unbiased Risk Estimator (SURE) to train a deep convolutional neural network without requiring denoised ground truth X-ray data. Our experimental results demonstrate the effectiveness of SURE based approach for denoising X-ray images.