  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.
