Department of Systems Design Engineering, University of Waterloo, Waterloo, Canada.
Waterloo Artificial Intelligence Institute, Waterloo, Canada.
Sci Rep. 2020 Nov 11;10(1):19549. doi: 10.1038/s41598-020-76550-z.
The Coronavirus Disease 2019 (COVID-19) pandemic continues to have a devastating effect on the health and well-being of the global population. A critical step in the fight against COVID-19 is effective screening of infected patients, with one of the key screening approaches being radiology examination using chest radiography. It was found in early studies that patients present abnormalities in chest radiography images that are characteristic of those infected with COVID-19. Motivated by this and inspired by the open source efforts of the research community, in this study we introduce COVID-Net, a deep convolutional neural network design tailored for the detection of COVID-19 cases from chest X-ray (CXR) images that is open source and available to the general public. To the best of the authors' knowledge, COVID-Net is one of the first open source network designs for COVID-19 detection from CXR images at the time of initial release. We also introduce COVIDx, an open access benchmark dataset that we generated comprising of 13,975 CXR images across 13,870 patient patient cases, with the largest number of publicly available COVID-19 positive cases to the best of the authors' knowledge. Furthermore, we investigate how COVID-Net makes predictions using an explainability method in an attempt to not only gain deeper insights into critical factors associated with COVID cases, which can aid clinicians in improved screening, but also audit COVID-Net in a responsible and transparent manner to validate that it is making decisions based on relevant information from the CXR images. By no means a production-ready solution, the hope is that the open access COVID-Net, along with the description on constructing the open source COVIDx dataset, will be leveraged and build upon by both researchers and citizen data scientists alike to accelerate the development of highly accurate yet practical deep learning solutions for detecting COVID-19 cases and accelerate treatment of those who need it the most.
2019 年冠状病毒病（COVID-19）大流行继续对全球人口的健康和福祉造成破坏性影响。抗击 COVID-19 的关键步骤之一是对感染患者进行有效筛查，其中关键的筛查方法之一是使用胸部 X 射线进行放射学检查。早期研究发现，感染 COVID-19 的患者的胸部 X 射线图像存在异常，这些异常具有特征性。受此启发，并受到研究社区开源工作的启发，在本研究中，我们引入了 COVID-Net，这是一种针对从胸部 X 射线（CXR）图像中检测 COVID-19 病例的深度卷积神经网络设计，该设计是开源的，可供公众使用。据作者所知，COVID-Net 是初始发布时第一个用于从 CXR 图像中检测 COVID-19 的开源网络设计之一。我们还引入了 COVIDx，这是一个开放获取的基准数据集，我们生成了该数据集，其中包含 13870 例患者的 13975 张 CXR 图像，是目前公开的 COVID-19 阳性病例数量最多的数据集。此外，我们还研究了 COVID-Net 如何使用可解释性方法进行预测，以便不仅深入了解与 COVID 病例相关的关键因素，从而帮助临床医生进行更好的筛查，而且还以负责任和透明的方式对 COVID-Net 进行审核，以验证它是根据 CXR 图像中的相关信息做出决策的。绝不是一种可立即投入使用的解决方案，我们希望开放访问的 COVID-Net 以及构建开源 COVIDx 数据集的说明，将被研究人员和公民数据科学家利用和进一步开发，以加速开发高度准确但实用的深度学习解决方案，用于检测 COVID-19 病例，并加速对最需要的患者的治疗。
