  Until now, Coronavirus SARS-CoV-2 has caused more than 850,000 deaths and
infected more than 27 million individuals in over 120 countries. Besides
principal polymerase chain reaction (PCR) tests, automatically identifying
positive samples based on computed tomography (CT) scans can present a
promising option in the early diagnosis of COVID-19. Recently, there have been
increasing efforts to utilize deep networks for COVID-19 diagnosis based on CT
scans. While these approaches mostly focus on introducing novel architectures,
transfer learning techniques, or construction large scale data, we propose a
novel strategy to improve the performance of several baselines by leveraging
multiple useful information sources relevant to doctors' judgments.
Specifically, infected regions and heat maps extracted from learned networks
are integrated with the global image via an attention mechanism during the
learning process. This procedure not only makes our system more robust to noise
but also guides the network focusing on local lesion areas. Extensive
experiments illustrate the superior performance of our approach compared to
recent baselines. Furthermore, our learned network guidance presents an
explainable feature to doctors as we can understand the connection between
input and output in a grey-box model.
