  A critical step in the fight against COVID-19, which continues to have a
catastrophic impact on peoples lives, is the effective screening of patients
presented in the clinics with severe COVID-19 symptoms. Chest radiography is
one of the promising screening approaches. Many studies reported detecting
COVID-19 in chest X-rays accurately using deep learning. A serious limitation
of many published approaches is insufficient attention paid to explaining
decisions made by deep learning models. Using explainable artificial
intelligence methods, we demonstrate that model decisions may rely on
confounding factors rather than medical pathology. After an analysis of
potential confounding factors found on chest X-ray images, we propose a novel
method to minimise their negative impact. We show that our proposed method is
more robust than previous attempts to counter confounding factors such as ECG
leads in chest X-rays that often influence model classification decisions. In
addition to being robust, our method achieves results comparable to the
state-of-the-art. The source code and pre-trained weights are publicly
available at (https://github.com/tomek1911/POTHER).
