论文标题
COVID-NET UV：端到端时空深度神经网络架构，用于从超声视频中自动诊断Covid-19感染的自动诊断
COVID-Net UV: An End-to-End Spatio-Temporal Deep Neural Network Architecture for Automated Diagnosis of COVID-19 Infection from Ultrasound Videos
论文作者
论文摘要
除了疫苗接种外，作为减轻Covid-19的进一步传播的有效方法，还必须快速准确地筛查个人以确保公共卫生安全是必要的。我们提出了Covid-net UV，这是一种端到端混合时空深神经网络结构，以检测来自凸出传感器捕获的肺部超声超声视频的COVID-19感染。 COVID-NET UV包括一个卷积神经网络，该网络提取空间特征和学习时间依赖性的复发性神经网络。经过仔细的高参数调整后，该网络的平均准确性为94.44％，没有用于19009病例的假阴性病例。 Covid-NET UV的目标是通过加速筛查肺部护理超声视频和自动检测COVID-19-19阳性病例的筛查，以协助前线临床医生与COVID-19斗争。
Besides vaccination, as an effective way to mitigate the further spread of COVID-19, fast and accurate screening of individuals to test for the disease is yet necessary to ensure public health safety. We propose COVID-Net UV, an end-to-end hybrid spatio-temporal deep neural network architecture, to detect COVID-19 infection from lung point-of-care ultrasound videos captured by convex transducers. COVID-Net UV comprises a convolutional neural network that extracts spatial features and a recurrent neural network that learns temporal dependence. After careful hyperparameter tuning, the network achieves an average accuracy of 94.44% with no false-negative cases for COVID-19 cases. The goal with COVID-Net UV is to assist front-line clinicians in the fight against COVID-19 via accelerating the screening of lung point-of-care ultrasound videos and automatic detection of COVID-19 positive cases.
