About: Hypergraph Learning for Identification of COVID-19 with CT Imaging

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Hypergraph Learning for Identification of COVID-19 with CT Imaging
Creator	Xia, Liming Yan, Fuhua Shi, Feng Wei, Ying Shao, Ying Shan, Fei Gao, Yue Di, Donglin Ding, Zhongxiang Gao, Yaozong Han, Miaofei Li, Shengrui Mo, Zhanhao Shen, Dinggang Sui, He
Source	ArXiv
abstract	The coronavirus disease, named COVID-19, has become the largest global public health crisis since it started in early 2020. CT imaging has been used as a complementary tool to assist early screening, especially for the rapid identification of COVID-19 cases from community acquired pneumonia (CAP) cases. The main challenge in early screening is how to model the confusing cases in the COVID-19 and CAP groups, with very similar clinical manifestations and imaging features. To tackle this challenge, we propose an Uncertainty Vertex-weighted Hypergraph Learning (UVHL) method to identify COVID-19 from CAP using CT images. In particular, multiple types of features (including regional features and radiomics features) are first extracted from CT image for each case. Then, the relationship among different cases is formulated by a hypergraph structure, with each case represented as a vertex in the hypergraph. The uncertainty of each vertex is further computed with an uncertainty score measurement and used as a weight in the hypergraph. Finally, a learning process of the vertex-weighted hypergraph is used to predict whether a new testing case belongs to COVID-19 or not. Experiments on a large multi-center pneumonia dataset, consisting of 2,148 COVID-19 cases and 1,182 CAP cases from five hospitals, are conducted to evaluate the performance of the proposed method. Results demonstrate the effectiveness and robustness of our proposed method on the identification of COVID-19 in comparison to state-of-the-art methods.
has issue date	2020-05-07(xsd:dateTime)
has license	arxiv
sha1sum (hex)	0401ddd31ae599dfa12a41d7de6b2cf626065157
resource representing a document's title	Hypergraph Learning for Identification of COVID-19 with CT Imaging
resource representing a document's body	covid:0401ddd31ae599dfa12a41d7de6b2cf626065157#body_text
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