  The global outbreak of the Coronavirus 2019 (COVID-19) has overloaded
worldwide healthcare systems. Computer-aided diagnosis for COVID-19 fast
detection and patient triage is becoming critical. This paper proposes a novel
self-knowledge distillation based self-supervised learning method for COVID-19
detection from chest X-ray images. Our method can use self-knowledge of images
based on similarities of their visual features for self-supervised learning.
Experimental results show that our method achieved an HM score of 0.988, an AUC
of 0.999, and an accuracy of 0.957 on the largest open COVID-19 chest X-ray
dataset.
