# Synthesis of COVID-19 Chest X-rays using Unpaired Image-to-Image Translation

arXiv: https://arxiv.org/abs/2010.10266 · HTML: https://mlsanity.com/paper/2010.10266/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2020.

## Built on by (most cited first, 13 of 13)

- [DCFace: Synthetic Face Generation with Dual Condition Diffusion Model](https://mlsanity.com/paper/2304.07060/) (2023)
- [Deep Learning for Chest X-ray Analysis: A Survey](https://mlsanity.com/paper/2103.08700/) (2021)
- [NEVIS'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Research](https://mlsanity.com/paper/2211.11747/) (2022)
- [Synthetic Data in Radiological Imaging: Current State and Future Outlook](https://mlsanity.com/paper/2407.01561/) (2024)
- [Combating COVID-19 using Generative Adversarial Networks and Artificial Intelligence for Medical Images: A Scoping Review](https://mlsanity.com/paper/2205.07236/) (2022)
- [Leveraging GANs for data scarcity of COVID-19: Beyond the hype](https://mlsanity.com/paper/2304.03536/) (2023)
- [BioGAN: An unpaired GAN-based image to image translation model for microbiological images](https://mlsanity.com/paper/2306.06217/) (2023)
- [Synthetic COVID-19 Chest X-ray Dataset for Computer-Aided Diagnosis](https://mlsanity.com/paper/2106.09759/) (2021)
- [RADA: Robust Adversarial Data Augmentation for Camera Localization in Challenging Weather](https://mlsanity.com/paper/2112.02469/) (2021)
- [Enhancing Generative Networks for Chest Anomaly Localization through Automatic Registration-Based Unpaired-to-Pseudo-Paired Training Data Translation](https://mlsanity.com/paper/2207.10324/) (2022)
- [Synthetic data: How could it be used for infectious disease research?](https://mlsanity.com/paper/2407.06211/) (2024)
- [Sound Signal Synthesis with Auxiliary Classifier GAN, COVID-19 cough as an example](https://mlsanity.com/paper/2508.08892/) (2025)
- [Medical Image Classification on Imbalanced Data Using ProGAN and SMA-Optimized ResNet: Application to COVID-19](https://mlsanity.com/paper/2512.24214/) (2025)

## Abstract (arXiv)

Motivated by the lack of publicly available datasets of chest radiographs of positive patients with Coronavirus disease 2019 (COVID-19), we build the first-of-its-kind open dataset of synthetic COVID-19 chest X-ray images of high fidelity using an unsupervised domain adaptation approach by leveraging class conditioning and adversarial training. Our contributions are twofold. First, we show considerable performance improvements on COVID-19 detection using various deep learning architectures when employing synthetic images as additional training set. Second, we show how our image synthesis method can serve as a data anonymization tool by achieving comparable detection performance when trained only on synthetic data. In addition, the proposed data generation framework offers a viable solution to the COVID-19 detection in particular, and to medical image classification tasks in general. Our publicly available benchmark dataset consists of 21,295 synthetic COVID-19 chest X-ray images. The insights gleaned from this dataset can be used for preventive actions in the fight against the COVID-19 pandemic.