COVID-Net CT-S: 3D Convolutional Neural Network Architectures for COVID-19 Severity Assessment using Chest CT Images
This addresses the challenge of accurate severity assessment for COVID-19 patients, aiding radiologists and healthcare systems, though it is incremental as it builds on existing deep learning approaches.
The authors tackled COVID-19 severity assessment from chest CT images by introducing COVID-Net CT-S, a suite of 3D convolutional neural networks, which achieved significantly improved performance compared to traditional 2D methods.
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.