  This paper addresses the new problem of automated screening of coronavirus
disease 2019 (COVID-19) based on chest X-rays, which is urgently demanded
toward fast stopping the pandemic. However, robust and accurate screening of
COVID-19 from chest X-rays is still a globally recognized challenge because of
two bottlenecks: 1) imaging features of COVID-19 share some similarities with
other pneumonia on chest X-rays, and 2) the misdiagnosis rate of COVID-19 is
very high, and the misdiagnosis cost is expensive. While a few pioneering works
have made much progress, they underestimate both crucial bottlenecks. In this
paper, we report our solution, discriminative cost-sensitive learning (DCSL),
which should be the choice if the clinical needs the assisted screening of
COVID-19 from chest X-rays. DCSL combines both advantages from fine-grained
classification and cost-sensitive learning. Firstly, DCSL develops a
conditional center loss that learns deep discriminative representation.
Secondly, DCSL establishes score-level cost-sensitive learning that can
adaptively enlarge the cost of misclassifying COVID-19 examples into other
classes. DCSL is so flexible that it can apply in any deep neural network. We
collected a large-scale multi-class dataset comprised of 2,239 chest X-ray
examples: 239 examples from confirmed COVID-19 cases, 1,000 examples with
confirmed bacterial or viral pneumonia cases, and 1,000 examples of healthy
people. Extensive experiments on the three-class classification show that our
algorithm remarkably outperforms state-of-the-art algorithms. It achieves an
accuracy of 97.01%, a precision of 97%, a sensitivity of 97.09%, and an
F1-score of 96.98%. These results endow our algorithm as an efficient tool for
the fast large-scale screening of COVID-19.
