论文标题
COVID-19的护理点图像分析
Point of Care Image Analysis for COVID-19
论文作者
论文摘要
早期发现Covid-19是包含大流行的关键。基于成像的疾病检测和评估既快速又便宜，因此在Covid-19处理中起着重要作用。 COVID-19更容易在胸部CT中检测到，但是它昂贵，不可存放且难以消毒，这使其不适合护理点（POC）模式。另一方面，胸部X射线（CXR）和肺超声（LUS）被广泛使用，但是，这些方式中的COVID-19发现并不总是很清楚。在这里，我们训练深层神经网络，以显着增强使用CXR和LUS检测，分级和监测COVID-19患者的能力。与以色列的多家医院合作，我们收集了一个大型CXR数据集，并使用此数据集训练一个神经网络，该神经网络可获得COVID-19的90％以上检测率。此外，与Ultra（意大利的超声实验室Trento）和意大利的医院合作，我们获得了带有疾病严重性注释的POC超声数据，并培训了一个深层网络以进行自动严重性分级。
Early detection of COVID-19 is key in containing the pandemic. Disease detection and evaluation based on imaging is fast and cheap and therefore plays an important role in COVID-19 handling. COVID-19 is easier to detect in chest CT, however, it is expensive, non-portable, and difficult to disinfect, making it unfit as a point-of-care (POC) modality. On the other hand, chest X-ray (CXR) and lung ultrasound (LUS) are widely used, yet, COVID-19 findings in these modalities are not always very clear. Here we train deep neural networks to significantly enhance the capability to detect, grade and monitor COVID-19 patients using CXRs and LUS. Collaborating with several hospitals in Israel we collect a large dataset of CXRs and use this dataset to train a neural network obtaining above 90% detection rate for COVID-19. In addition, in collaboration with ULTRa (Ultrasound Laboratory Trento, Italy) and hospitals in Italy we obtained POC ultrasound data with annotations of the severity of disease and trained a deep network for automatic severity grading.
