COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images.
COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images.
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DOI：
10.1038/s41598-020-76550-z
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发表时间：
2020-11-11
影响因子：
4.6
通讯作者：
Wong A
中科院分区：
文献类型：
--
作者：
Wang L;Lin ZQ;Wong A
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产生破坏性影响。抗击 COVID-19 的关键一步是对感染患者进行有效筛查，其中关键筛查方法之一是使用胸部 X 光检查进行放射学检查。早期研究发现，患者胸部 X 线摄影图像出现异常，这是感染了 COVID-19 的患者的特征。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，这是一种深度卷积神经网络设计，专为从胸部 X 射线 (CXR) 图像中检测 COVID-19 病例而设计，该网络是开源的并向公众开放。据作者所知，COVID-Net 是最初发布时用于从 CXR 图像检测 COVID-19 的首批开源网络设计之一。我们还介绍了 COVIDx，这是一个开放获取基准数据集，我们生成的数据集包含 13,870 名患者病例的 13,975 张 CXR 图像，据作者所知，其中公开的 COVID-19 阳性病例数量最多。此外，我们研究了 COVID-Net 如何使用可解释性方法进行预测，不仅试图更深入地了解与 COVID 病例相关的关键因素，这可以帮助临床医生改进筛查，而且还以负责任和透明的方式审核 COVID-Net，以验证其是否根据 CXR 图像的相关信息做出决策。绝不是一个可立即投入生产的解决方案，我们希望研究人员和公民数据科学家能够利用和构建开放访问的 COVID-Net 以及有关构建开源 COVIDx 数据集的描述，以加速开发高精度且实用的深度学习解决方案，以检测 COVID-19 病例并加速对最需要的人的治疗。
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.