COVID-Net S: Towards computer-aided severity assessment via training and validation of deep neural networks for geographic extent and opacity extent scoring of chest X-rays for SARS-CoV-2 lung disease severity

2 code implementations • 26 May 2020 • Alexander Wong, Zhong Qiu Lin, Linda Wang, Audrey G. Chung, Beiyi Shen, Almas Abbasi, Mahsa Hoshmand-Kochi, Timothy Q. Duong

Findings: The COVID-Net S deep neural networks yielded R$^2$ of 0. 664 $\pm$ 0. 032 and 0. 635 $\pm$ 0. 044 between predicted scores and radiologist scores for geographic extent and opacity extent, respectively, in stratified Monte Carlo cross-validation experiments.

Predicting COVID-19 Pneumonia Severity on Chest X-ray with Deep Learning

6 code implementations • 24 May 2020 • Joseph Paul Cohen, Lan Dao, Paul Morrison, Karsten Roth, Yoshua Bengio, Beiyi Shen, Almas Abbasi, Mahsa Hoshmand-Kochi, Marzyeh Ghassemi, Haifang Li, Tim Q Duong

In this study, we present a severity score prediction model for COVID-19 pneumonia for frontal chest X-ray images.

Management