  The health and socioeconomic difficulties caused by the COVID-19 pandemic
continues to cause enormous tensions around the world. In particular, this
extraordinary surge in the number of cases has put considerable strain on
health care systems around the world. A critical step in the treatment and
management of COVID-19 positive patients is severity assessment, which is
challenging even for expert radiologists given the subtleties at different
stages of lung disease severity. Motivated by this challenge, we introduce
COVID-Net CT-S, a suite of deep convolutional neural networks for predicting
lung disease severity due to COVID-19 infection. More specifically, a 3D
residual architecture design is leveraged to learn volumetric visual indicators
characterizing the degree of COVID-19 lung disease severity. Experimental
results using the patient cohort collected by the China National Center for
Bioinformation (CNCB) showed that the proposed COVID-Net CT-S networks, by
leveraging volumetric features, can achieve significantly improved severity
assessment performance when compared to traditional severity assessment
networks that learn and leverage 2D visual features to characterize COVID-19
severity.
