  The limited availability of labeled chest X-ray datasets is a significant
bottleneck in the development of medical imaging methods. Self-supervised
learning (SSL) can mitigate this problem by training models on unlabeled data.
Furthermore, self-supervised pretraining has yielded promising results in
visual recognition of natural images but has not been given much consideration
in medical image analysis. In this work, we propose a self-supervised method,
DINO-CXR, which is a novel adaptation of a self-supervised method, DINO, based
on a vision transformer for chest X-ray classification. A comparative analysis
is performed to show the effectiveness of the proposed method for both
pneumonia and COVID-19 detection. Through a quantitative analysis, it is also
shown that the proposed method outperforms state-of-the-art methods in terms of
accuracy and achieves comparable results in terms of AUC and F-1 score while
requiring significantly less labeled data.
