  Recently, the outbreak of Coronavirus Disease 2019 (COVID-19) has spread
rapidly across the world. Due to the large number of affected patients and
heavy labor for doctors, computer-aided diagnosis with machine learning
algorithm is urgently needed, and could largely reduce the efforts of
clinicians and accelerate the diagnosis process. Chest computed tomography (CT)
has been recognized as an informative tool for diagnosis of the disease. In
this study, we propose to conduct the diagnosis of COVID-19 with a series of
features extracted from CT images. To fully explore multiple features
describing CT images from different views, 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.
Specifically, 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) and also a large margin is guaranteed between different types
of pneumonia. In this way, our model can well avoid overfitting compared to the
case of directly projecting highdimensional features into classes. Extensive
experimental results show that the proposed method outperforms all comparison
methods, and rather stable performances are observed when varying the numbers
of training data.
