  In this paper, our focus is on constructing models to assist a clinician in
the diagnosis of COVID-19 patients in situations where it is easier and cheaper
to obtain X-ray data than to obtain high-quality images like those from CT
scans. Deep neural networks have repeatedly been shown to be capable of
constructing highly predictive models for disease detection directly from image
data. However, their use in assisting clinicians has repeatedly hit a stumbling
block due to their black-box nature. Some of this difficulty can be alleviated
if predictions were accompanied by explanations expressed in clinically
relevant terms. In this paper, deep neural networks are used to extract
domain-specific features(morphological features like ground-glass opacity and
disease indications like pneumonia) directly from the image data. Predictions
about these features are then used to construct a symbolic model (a decision
tree) for the diagnosis of COVID-19 from chest X-rays, accompanied with two
kinds of explanations: visual (saliency maps, derived from the neural stage),
and textual (logical descriptions, derived from the symbolic stage). A
radiologist rates the usefulness of the visual and textual explanations. Our
results demonstrate that neural models can be employed usefully in identifying
domain-specific features from low-level image data; that textual explanations
in terms of clinically relevant features may be useful; and that visual
explanations will need to be clinically meaningful to be useful.
