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

• URL: http://arxiv.org/abs/2010.10266v1
• Date: Tue, 20 Oct 2020 13:37:40 GMT
• Title: Synthesis of COVID-19 Chest X-rays using Unpaired Image-to-Image Translation
• Authors: Hasib Zunair and A. Ben Hamza
• Abstract summary: 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. We show considerable performance improvements on COVID-19 detection using various deep learning architectures. Our publicly available benchmark dataset consists of 21,295 synthetic COVID-19 chest X-ray images.
• Score: 6.22964000148682
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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