  The novel 2019 Coronavirus disease (COVID-19) global pandemic is a defining
health crisis. Recent efforts have been increasingly directed towards achieving
quick and accurate detection of COVID-19 across symptomatic patients to
mitigate the intensity and spread of the disease. Artificial intelligence (AI)
algorithms applied to chest X-ray (CXR) images have emerged as promising
diagnostic tools, and previous work has demonstrated impressive classification
performances. However, such methods have faced criticisms from physicians due
to their black-box reasoning process and unpredictable nature. In contrast to
professional radiologist diagnosis, AI systems often lack generalizability,
explainability, and robustness in the clinical decision making process. In our
work, we address these issues by first proposing an extensive baseline study,
training and evaluating 21 convolutional neural network (CNN) models on a
diverse set of 33,000+ CXR images to classify between healthy, COVID-19, and
non-COVID-19 pneumonia CXRs. Our resulting models achieved a 3-way
classification accuracy, recall, and precision of up to 97.03\%, 97.97\%, and
99.95\%, respectively. Next, we investigate the effectiveness of adversarial
training on model robustness and explainability via Gradient-weighted Class
Activation Mapping (Grad-CAM) heatmaps. We find that adversarially trained
models not only significantly outperform their standard counterparts on
classifying perturbed images, but also yield saliency maps that 1) better
specify clinically relevant features, 2) are robust against extraneous
artifacts, and 3) agree considerably more with expert radiologist findings.
