id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-299932-c079r94n	He, X.	Benchmarking Deep Learning Models and Automated Model Design for COVID-19 Detection with Chest CT Scans	2020-06-09		.txt	text/plain	6200	380	63	Although many recent studies have shown that deep learning based solutions can help detect COVID-19 based on chest CT scans, there lacks a consistent and systematic comparison and evaluation on these techniques. Therefore, many recent studies have tried to use deep learning (DL) methods to assist COVID-19 diagnosis with chest X-rays or CT scan images. In this paper, we use our dataset to benchmark two types of state-of-the-art (SOTA) DL models: 1) 3D convolutional neural networks (CNNs), including DenseNet3D121 [17] , R2Plus1D [18] , MC3 18 [18] , ResNeXt3D101 [17] , Pre-Act ResNet [17] , and ResNet3D series [17] ; 2) 2D CNNs, including DenseNet121 [19] , DenseNet201 [19] , ResNet50 [20] , ResNet101 [20] and ResNeXt101 [21] . Instead, the model trained on scan data with a small number of slices can also achieve comparable or even better results.	./cache/cord-299932-c079r94n.txt	./txt/cord-299932-c079r94n.txt