论文标题
Pocovid-net：从新的肺超声成像数据集（POCUS）自动检测COVID-19
POCOVID-Net: Automatic Detection of COVID-19 From a New Lung Ultrasound Imaging Dataset (POCUS)
论文作者
论文摘要
随着COVID-19成为全球大流行的迅速发展，廉价，快速和可靠的工具迫切需要可以帮助医生诊断Covid-19。诸如CT之类的医学成像可以在补充分子生物学的常规诊断工具中发挥关键作用，并且使用深度学习技术，使用CT或X射线数据证明了几种自动系统的表现。在这里，我们提倡在Pare Point超声成像中发挥更为突出的作用，以指导Covid-19检测。在全球医疗设施中，超声是无创和无处不在的。我们的贡献是三倍。首先，我们收集了一个由1103张图像（654 Covid-19，277个细菌性肺炎和172个健康对照组）组成的肺超声（POCUS）数据集，并从64个视频中取样。该数据集是从各种在线来源组装的，专门针对深度学习模型进行了处理，旨在作为开放访问计划的起点。其次，我们在此3级数据集上培训了深层卷积神经网络（Pocovid-net），并获得了89％的精度，并且以大多数投票为92％的视频准确性。对于特别检测COVID-19，模型的灵敏度为0.96，在5倍的交叉验证中的特异性为0.79，F1得分为0.92。第三，我们提供一个开放式Web服务（PocovidScreen），可在以下网址提供：https：//pocovidscreen.org。该网站部署了预测模型，可以对超声肺图像进行预测。此外，它还为医务人员提供了（批量）上传自己的筛查的选项，以便为不断增长的病理肺超声图像的公共数据库做出贡献。 数据集和代码可从：https：//github.com/jannisborn/covid19_pocus_ultrasound获得。 注意：此预印本被我们的应用科学论文所取代：https：//doi.org/10.3390/App11020672
With the rapid development of COVID-19 into a global pandemic, there is an ever more urgent need for cheap, fast and reliable tools that can assist physicians in diagnosing COVID-19. Medical imaging such as CT can take a key role in complementing conventional diagnostic tools from molecular biology, and, using deep learning techniques, several automatic systems were demonstrated promising performances using CT or X-ray data. Here, we advocate a more prominent role of point-of-care ultrasound imaging to guide COVID-19 detection. Ultrasound is non-invasive and ubiquitous in medical facilities around the globe. Our contribution is threefold. First, we gather a lung ultrasound (POCUS) dataset consisting of 1103 images (654 COVID-19, 277 bacterial pneumonia and 172 healthy controls), sampled from 64 videos. This dataset was assembled from various online sources, processed specifically for deep learning models and is intended to serve as a starting point for an open-access initiative. Second, we train a deep convolutional neural network (POCOVID-Net) on this 3-class dataset and achieve an accuracy of 89% and, by a majority vote, a video accuracy of 92% . For detecting COVID-19 in particular, the model performs with a sensitivity of 0.96, a specificity of 0.79 and F1-score of 0.92 in a 5-fold cross validation. Third, we provide an open-access web service (POCOVIDScreen) that is available at: https://pocovidscreen.org. The website deploys the predictive model, allowing to perform predictions on ultrasound lung images. In addition, it grants medical staff the option to (bulk) upload their own screenings in order to contribute to the growing public database of pathological lung ultrasound images. Dataset and code are available from: https://github.com/jannisborn/covid19_pocus_ultrasound. NOTE: This preprint is superseded by our paper in Applied Sciences: https://doi.org/10.3390/app11020672
