960 citations · 970 across the 3 of their papers we have counts for
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eess.IV2020
Predicting COVID-19 Pneumonia Severity on Chest X-ray with Deep Learning
Joseph Paul Cohen, Lan Dao, Paul Morrison +8
Purpose: The need to streamline patient management for COVID-19 has become more pressing than ever. Chest X-rays provide a non-invasive (potentially bedside) tool to monitor the pr…
eess.IV2020★ 9 cited
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
Alexander Wong, Zhong Qiu Lin, Linda Wang +5
Background: A critical step in effective care and treatment planning for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of the COVID-19 pandemic, is the as…