  Chest radiography is a relatively cheap, widely available medical procedure
that conveys key information for making diagnostic decisions. Chest X-rays are
almost always used in the diagnosis of respiratory diseases such as pneumonia
or the recent COVID-19. In this paper, we propose a self-supervised deep neural
network that is pretrained on an unlabeled chest X-ray dataset. The learned
representations are transferred to downstream task - the classification of
respiratory diseases. The results obtained on four public datasets show that
our approach yields competitive results without requiring large amounts of
labeled training data.
