id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-151667-nz26lxyk	Born, Jannis	Accelerating COVID-19 Differential Diagnosis with Explainable Ultrasound Image Analysis	2020-09-13		.txt	text/plain	5985	275	48	We provide the largest publicly available lung ultrasound (US) dataset for COVID-19 consisting of 106 videos from three classes (COVID-19, bacterial pneumonia, and healthy controls); curated and approved by medical experts. Here, we provide the first study of automatic lung ultrasound analysis for differential diagnosis of bacterial and viral pneumonia; aiming to develop a medical decision support tool. Literature on exploiting medical image analysis and computer vision techniques to classify or segment CT or CXR data of COVID-19 patients recently exploded (for reviews, see Shi et al. In comparison to a naïve, frame-based video classifier (obtained by averaging scores of all frames), we also investigate Models Genesis, a generic model for 3D medical image analysis pretrained on lung CT scans [54] . Concerning per-class prediction accuracies, it is evident that bacterial pneumonia infections are distinguished best, with recall, precision, and specificity above 0.93 for VGG and VGG-CAM, indicating the models' ability to recognize strong irregularities in lung images.	./cache/cord-151667-nz26lxyk.txt	./txt/cord-151667-nz26lxyk.txt