  Identifying who is infected with the Covid-19 virus is critical for
controlling its spread. X-ray machines are widely available worldwide and can
quickly provide images that can be used for diagnosis. A number of recent
studies claim it may be possible to build highly accurate models, using deep
learning, to detect Covid-19 from chest X-ray images. This paper explores the
robustness and generalization ability of convolutional neural network models in
diagnosing Covid-19 disease from frontal-view (AP/PA), raw chest X-ray images
that were lung field cropped. Some concerning observations are made about high
performing models that have learned to rely on confounding features related to
the data source, rather than the patient's lung pathology, when differentiating
between Covid-19 positive and negative labels. Specifically, these models
likely made diagnoses based on confounding factors such as patient age or image
processing artifacts, rather than medically relevant information.
