  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.
