论文标题
Covid-net：量身定制的深卷积神经网络设计，用于检测胸部X射线图像的COVID-19病例
COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images
论文作者
论文摘要
Covid-19的大流行对全球人口的健康和福祉产生了毁灭性的影响。与COVID-19作战的关键步骤是对感染患者进行有效筛查，其中一种关键筛查方法是使用胸部射线照相检查的放射学检查。受到研究界的开源工作的启发，在这项研究中，我们介绍了Covid-Net，这是一种量身定制的深卷积神经网络设计，该设计旨在检测开源源并提供给普通大众的Covid-19案例。据作者所知，Covid-Net是首次在初次发布时从CXR图像中检测到COVID-19检测的开源网络设计之一。我们还介绍了Covidx，这是一个开放访问基准数据集，我们在13,870名患者病例中生成了13,975个CXR图像，其中最多的公开COVID-19正案例，据作者所知。此外，我们研究了Covid-net如何使用一种解释性方法进行预测，以尝试更深入地了解与共同情况相关的关键因素，这可以帮助临床医生改善筛查，还以负责任的透明方式审核Covid-Net，以验证基于CXR图像的相关信息，以验证其根据CXR图像做出的决定。绝不是一种准备生产的解决方案，希望开放访问Covid-NET以及构建开源Covidx数据集的描述将由研究人员和公民数据科学家都得到利用和建立，以加速发展高度准确而实用的深度学习解决方案，以检测Covid-19案例并加速需要治疗的人。
The 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. 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.
