  The world is currently experiencing an ongoing pandemic of an infectious
disease named coronavirus disease 2019 (i.e., COVID-19), which is caused by the
severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Computed
Tomography (CT) plays an important role in assessing the severity of the
infection and can also be used to identify those symptomatic and asymptomatic
COVID-19 carriers. With a surge of the cumulative number of COVID-19 patients,
radiologists are increasingly stressed to examine the CT scans manually.
Therefore, an automated 3D CT scan recognition tool is highly in demand since
the manual analysis is time-consuming for radiologists and their fatigue can
cause possible misjudgment. However, due to various technical specifications of
CT scanners located in different hospitals, the appearance of CT images can be
significantly different leading to the failure of many automated image
recognition approaches. The multi-domain shift problem for the multi-center and
multi-scanner studies is therefore nontrivial that is also crucial for a
dependable recognition and critical for reproducible and objective diagnosis
and prognosis. In this paper, we proposed a COVID-19 CT scan recognition model
namely coronavirus information fusion and diagnosis network (CIFD-Net) that can
efficiently handle the multi-domain shift problem via a new robust weakly
supervised learning paradigm. Our model can resolve the problem of different
appearance in CT scan images reliably and efficiently while attaining higher
accuracy compared to other state-of-the-art methods.
