论文标题
COVIDNET-CT：一种量身定制的深卷积神经网络设计，用于检测COVID-19的胸部CT图像案例
COVIDNet-CT: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest CT Images
论文作者
论文摘要
2019年冠状病毒病（COVID-19）大流行对世界各地的患者和医疗保健系统产生巨大影响。在与这种新型疾病的斗争中，迫切需要快速有效的筛查工具来识别感染了Covid-19的患者，并且已经提出了CT成像，这是一种关键筛查方法之一，可以用作RT-PCR测试的补充，尤其是在患者中遇到不断疾病的患者，以使其ersive的患者遇到了不断变化的病人，以使患者遇到不利的疾病患者，以使患者进行调查的患者，以进行调查。为Covid-19阳性，但RT-PCR测试结果为负。在这项研究中，我们介绍了Covidnet-CT，这是一种深层卷积神经网络体系结构，该构造是针对通过机器驱动的设计探索方法从胸部CT图像中检测到Covid-19病例的量身定制的。此外，我们介绍了Covidx-CT，这是一种基准的CT图像数据集，该数据集源自中国国家生物信息中心收集的CT成像数据，其中包括1,489例患者病例，其中包括104,009张图像。此外，出于可靠性和透明度的利益，我们利用解释性驱动的性能验证策略来研究Covidnet-CT的决策行为，并确保Covidnet-CT基于CT图像中的相关指标做出预测。 COVIDNET-CT和COVIDX-CT数据集均以开源和开放访问方式向公众提供，作为Covid-Net计划的一部分。尽管Covidnet-CT尚未成为生产就绪的筛查解决方案，但我们希望发布模型和数据集将鼓励研究人员，临床医生和公民数据科学家都在利用和建立这些科学家。
The coronavirus disease 2019 (COVID-19) pandemic continues to have a tremendous impact on patients and healthcare systems around the world. In the fight against this novel disease, there is a pressing need for rapid and effective screening tools to identify patients infected with COVID-19, and to this end CT imaging has been proposed as one of the key screening methods which may be used as a complement to RT-PCR testing, particularly in situations where patients undergo routine CT scans for non-COVID-19 related reasons, patients with worsening respiratory status or developing complications that require expedited care, and patients suspected to be COVID-19-positive but have negative RT-PCR test results. Motivated by this, in this study we introduce COVIDNet-CT, a deep convolutional neural network architecture that is tailored for detection of COVID-19 cases from chest CT images via a machine-driven design exploration approach. Additionally, we introduce COVIDx-CT, a benchmark CT image dataset derived from CT imaging data collected by the China National Center for Bioinformation comprising 104,009 images across 1,489 patient cases. Furthermore, in the interest of reliability and transparency, we leverage an explainability-driven performance validation strategy to investigate the decision-making behaviour of COVIDNet-CT, and in doing so ensure that COVIDNet-CT makes predictions based on relevant indicators in CT images. Both COVIDNet-CT and the COVIDx-CT dataset are available to the general public in an open-source and open access manner as part of the COVID-Net initiative. While COVIDNet-CT is not yet a production-ready screening solution, we hope that releasing the model and dataset will encourage researchers, clinicians, and citizen data scientists alike to leverage and build upon them.
