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.
@article{arxiv.2103.03055,
title = {Self-supervised deep convolutional neural network for chest X-ray classification},
author = {Matej Gazda and Jakub Gazda and Jan Plavka and Peter Drotar},
journal= {arXiv preprint arXiv:2103.03055},
year = {2021}
}
Comments
The work was published by IEEE Access. DOI: 10.1109/ACCESS.2021.3125324