  Under the global pandemic of COVID-19, the use of artificial intelligence to
analyze chest X-ray (CXR) image for COVID-19 diagnosis and patient triage is
becoming important. Unfortunately, due to the emergent nature of the COVID-19
pandemic, a systematic collection of the CXR data set for deep neural network
training is difficult. To address this problem, here we propose a patch-based
convolutional neural network approach with a relatively small number of
trainable parameters for COVID-19 diagnosis. The proposed method is inspired by
our statistical analysis of the potential imaging biomarkers of the CXR
radiographs. Experimental results show that our method achieves
state-of-the-art performance and provides clinically interpretable saliency
maps, which are useful for COVID-19 diagnosis and patient triage.
