# Noise as Domain Shift: Denoising Medical Images by Unpaired Image Translation

arXiv: https://arxiv.org/abs/1910.02702 · HTML: https://mlsanity.com/paper/1910.02702/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2019.

## Built on by (most cited first, 8 of 8)

- [Domain Adaptation for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2102.09508/) (2021)
- [Image-to-Image Translation: Methods and Applications](https://mlsanity.com/paper/2101.08629/) (2021)
- [Enhancing and Adapting in the Clinic: Source-free Unsupervised Domain Adaptation for Medical Image Enhancement](https://mlsanity.com/paper/2312.01338/) (2023)
- [Noise transfer for unsupervised domain adaptation of retinal OCT images](https://mlsanity.com/paper/2209.08097/) (2022)
- [Mind The Gap: Alleviating Local Imbalance for Unsupervised Cross-Modality Medical Image Segmentation](https://mlsanity.com/paper/2205.11888/) (2022)
- [Whole-body PET image denoising for reduced acquisition time](https://mlsanity.com/paper/2303.16085/) (2023)
- [Deep learning-based bias transfer for overcoming laboratory differences of microscopic images](https://mlsanity.com/paper/2105.11765/) (2021)
- [Generalizable Denoising of Microscopy Images using Generative Adversarial Networks and Contrastive Learning](https://mlsanity.com/paper/2303.15214/) (2023)

## Abstract (arXiv)

We cast the problem of image denoising as a domain translation problem between high and low noise domains. By modifying the cycleGAN model, we are able to learn a mapping between these domains on unpaired retinal optical coherence tomography images. In quantitative measurements and a qualitative evaluation by ophthalmologists, we show how this approach outperforms other established methods. The results indicate that the network differentiates subtle changes in the level of noise in the image. Further investigation of the model's feature maps reveals that it has learned to distinguish retinal layers and other distinct regions of the images.