  COVID-19 is a novel infectious disease responsible for over 800K deaths
worldwide as of August 2020. The need for rapid testing is a high priority and
alternative testing strategies including X-ray image classification are a
promising area of research. However, at present, public datasets for COVID19
x-ray images have low data volumes, making it challenging to develop accurate
image classifiers. Several recent papers have made use of Generative
Adversarial Networks (GANs) in order to increase the training data volumes. But
realistic synthetic COVID19 X-rays remain challenging to generate. We present a
novel Mean Teacher + Transfer GAN (MTT-GAN) that generates COVID19 chest X-ray
images of high quality. In order to create a more accurate GAN, we employ
transfer learning from the Kaggle Pneumonia X-Ray dataset, a highly relevant
data source orders of magnitude larger than public COVID19 datasets.
Furthermore, we employ the Mean Teacher algorithm as a constraint to improve
stability of training. Our qualitative analysis shows that the MTT-GAN
generates X-ray images that are greatly superior to a baseline GAN and visually
comparable to real X-rays. Although board-certified radiologists can
distinguish MTT-GAN fakes from real COVID19 X-rays. Quantitative analysis shows
that MTT-GAN greatly improves the accuracy of both a binary COVID19 classifier
as well as a multi-class Pneumonia classifier as compared to a baseline GAN.
Our classification accuracy is favourable as compared to recently reported
results in the literature for similar binary and multi-class COVID19 screening
tasks.
