Deep learning (DL) has proved successful in medical imaging and, in the wake of the recent COVID-19 pandemic, some works have started to investigate DLbased solutions for the assisted diagnosis of lung diseases. While existing works focus on CT scans, this paper studies the application of DL techniques for the analysis of lung ultrasonography (LUS) images. Specifically, we present a novel fully-annotated dataset of LUS images collected from several Italian hospitals, with labels indicating the degree of disease severity at a frame-level, videolevel, and pixel-level (segmentation masks). Leveraging these data, we introduce several deep models that address relevant tasks for the automatic analysis of LUS images. In particular, we present a novel deep network, derived from Spatial Transformer Networks, which simultaneously predicts the disease severity score associated to a input frame and provides localization of pathological artefacts in a weakly-supervised way. Furthermore, we introduce a new method based on uninorms for effective frame score aggregation at a video-level. Finally, we benchmark state of the art deep models for estimating pixel-level segmentations of COVID-19 imaging biomarkers. Experiments on the proposed dataset demonstrate satisfactory results on all the considered tasks, paving the way to future research on DL for the assisted diagnosis of COVID-19 from LUS data.

中文翻译：

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护理点肺超声中COVID-19标记分类和定位的深度学习。

事实证明，深度学习（DL）在医学成像方面是成功的，并且随着最近的COVID-19大流行，一些工作已开始研究基于DL的解决方案，以辅助诊断肺部疾病。虽然现有的工作集中在CT扫描上，但本文研究了DL技术在分析肺部超声（LUS）图像中的应用。具体来说，我们展示了从意大利多家医院收集的LUS图像的完整注释新数据集，其中的标签指示了帧级别，视频级别和像素级别（分割蒙版）的疾病严重程度。利用这些数据，我们介绍了一些深度模型，这些模型解决了有关LUS图像自动分析的相关任务。特别是，我们提出了一种新颖的深层网络，它是从空间变压器网络派生而来的，它同时预测与输入帧相关的疾病严重程度评分，并以弱监督的方式提供病理假象的定位。此外，我们介绍了一种基于单声道的新方法，用于在视频级别有效地进行帧分数聚合。最后，我们对最先进的深度模型进行基准测试，以估计COVID-19成像生物标记物的像素级细分。在建议的数据集上进行的实验表明，在所有考虑的任务上都取得了令人满意的结果，从而为将来从LUS数据诊断COVID-19的DL研究铺平了道路。我们对最先进的深度模型进行基准测试，以估算COVID-19成像生物标记物的像素级细分。在建议的数据集上进行的实验表明，在所有考虑的任务上都取得了令人满意的结果，从而为将来从LUS数据诊断COVID-19的DL研究铺平了道路。我们对最先进的深度模型进行基准测试，以估计COVID-19成像生物标记物的像素级细分。在建议的数据集上进行的实验表明，在所有考虑的任务上都取得了令人满意的结果，从而为将来从LUS数据诊断COVID-19的DL研究铺平了道路。