Huan Yuan

TL;DR: Wang et al. as mentioned in this paper developed a dual-sampling attention network to automatically diagnose COVID-19 from the community acquired pneumonia (CAP) in chest computed tomography (CT), and proposed a novel online attention module with a 3D convolutional network (CNN) to focus on the infection regions in lungs when making decisions of diagnoses.

TL;DR: An infection size-aware random forest method (iSARF) was proposed for discriminating COVID-19 from CAP and yielded its best performance when using the handcrafted features, with a sensitivity and accuracy of 90.7%, a specificity and an accuracy of 89.4% over state-of-the-art classifiers.

TL;DR: In this article, a unified latent representation is learned which can completely encode information from different aspects of features and is endowed with promising class structure for separability, while the completeness is guaranteed with a group of backward neural networks (each for one type of features), while by using class labels the representation is enforced to be compact within COVID-19/community-acquired pneumonia (CAP).

TL;DR: In this article, a set of handcrafted location-specific features was proposed to best capture the COVID-19 distribution pattern, in comparison to conventional CT severity score (CT-SS) and Radiomics features.

TL;DR: Experimental results on the CO VID-19 dataset suggest that the proposed AFS-DF achieves superior performance in COVID-19 vs. CAP classification, compared with 4 widely used machine learning methods.