# Synthetic COVID-19 Chest X-ray Dataset for Computer-Aided Diagnosis

arXiv: https://arxiv.org/abs/2106.09759 · HTML: https://mlsanity.com/paper/2106.09759/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 16 references that are in the map.
- First posted on arXiv 17 Jun 2021.

## Builds on (most cited first, 10 of 10)

- [MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs](https://mlsanity.com/paper/1901.07042/) (2019)
- [PadChest: A large chest x-ray image dataset with multi-label annotated reports](https://mlsanity.com/paper/1901.07441/) (2019)
- [COVID-19 Image Data Collection: Prospective Predictions Are the Future](https://mlsanity.com/paper/2006.11988/) (2020)
- [Automatic Detection of Coronavirus Disease (COVID-19) Using X-ray Images and Deep Convolutional Neural Networks](https://mlsanity.com/paper/2003.10849/) (2020)
- [COVID-ResNet: A Deep Learning Framework for Screening of COVID19 from Radiographs](https://mlsanity.com/paper/2003.14395/) (2020)
- [Automatic Detection of Coronavirus Disease (COVID-19) in X-ray and CT Images: A Machine Learning-Based Approach](https://mlsanity.com/paper/2004.10641/) (2020)
- [Melanoma Detection using Adversarial Training and Deep Transfer Learning](https://mlsanity.com/paper/2004.06824/) (2020)
- [Synthesis of COVID-19 Chest X-rays using Unpaired Image-to-Image Translation](https://mlsanity.com/paper/2010.10266/) (2020)
- [DeepCOVIDExplainer: Explainable COVID-19 Diagnosis Based on Chest X-ray Images](https://mlsanity.com/paper/2004.04582/) (2020)
- [COVID-MobileXpert: On-Device COVID-19 Patient Triage and Follow-up using Chest X-rays](https://mlsanity.com/paper/2004.03042/) (2020)

## Built on by (most cited first, 1 of 1)

- [Sound Signal Synthesis with Auxiliary Classifier GAN, COVID-19 cough as an example](https://mlsanity.com/paper/2508.08892/) (2025)

## Abstract (arXiv)

We introduce a new dataset called Synthetic COVID-19 Chest X-ray Dataset for training machine learning models. The dataset consists of 21,295 synthetic COVID-19 chest X-ray images to be used for computer-aided diagnosis. These images, generated via an unsupervised domain adaptation approach, are of high quality. We find that the synthetic images not only improve performance of various deep learning architectures when used as additional training data under heavy imbalance conditions, but also detect the target class with high confidence. We also find that comparable performance can also be achieved when trained only on synthetic images. Further, salient features of the synthetic COVID-19 images indicate that the distribution is significantly different from Non-COVID-19 classes, enabling a proper decision boundary. We hope the availability of such high fidelity chest X-ray images of COVID-19 will encourage advances in the development of diagnostic and/or management tools.