DIAGNOSIS OF COVID-19 VIA MULTI-LAYER MULTI-CENTER GRAPH ATTENTION NETWORK

Coronavirus disease in 2019 (COVID-19) is a global epidemic, which has affected more than billions of people worldwide. Its intelligent diagnosis based on imaging data has attracted lots of attention, but the heterogeneity between datasets renders its diagnosis challenging. To solve this problem, we propose a new multi-layer multi-center graph attention network (MM-GAN) for COVID-19 diagnosis based on computed tomography (CT) data. First, we use the 3D backbone network to extract feature. Second, we construct a multi-layer multi-center map by using extracted features and auxiliary information, which takes into account the heterogeneity between centers. Third, we use graph attention to generate a new graph structure and learn node representation. Finally, we input the multi-layer multi-center graph into the graph convolution to achieve the COVID-19 detection. Experiments on four multi-center datasets show that the framework is effective and outperforms the traditional classification methods.