  The coronavirus disease 2019 (COVID-19) pandemic continues to have a
tremendous impact on patients and healthcare systems around the world. In the
fight against this novel disease, there is a pressing need for rapid and
effective screening tools to identify patients infected with COVID-19, and to
this end CT imaging has been proposed as one of the key screening methods which
may be used as a complement to RT-PCR testing, particularly in situations where
patients undergo routine CT scans for non-COVID-19 related reasons, patients
with worsening respiratory status or developing complications that require
expedited care, and patients suspected to be COVID-19-positive but have
negative RT-PCR test results. Motivated by this, in this study we introduce
COVIDNet-CT, a deep convolutional neural network architecture that is tailored
for detection of COVID-19 cases from chest CT images via a machine-driven
design exploration approach. Additionally, we introduce COVIDx-CT, a benchmark
CT image dataset derived from CT imaging data collected by the China National
Center for Bioinformation comprising 104,009 images across 1,489 patient cases.
Furthermore, in the interest of reliability and transparency, we leverage an
explainability-driven performance validation strategy to investigate the
decision-making behaviour of COVIDNet-CT, and in doing so ensure that
COVIDNet-CT makes predictions based on relevant indicators in CT images. Both
COVIDNet-CT and the COVIDx-CT dataset are available to the general public in an
open-source and open access manner as part of the COVID-Net initiative. While
COVIDNet-CT is not yet a production-ready screening solution, we hope that
releasing the model and dataset will encourage researchers, clinicians, and
citizen data scientists alike to leverage and build upon them.
