Attention-based Multi-flow Network for COVID-19 Classification and Lesion Localization from Chest CT

Abstract

COVID-19 has been rapidly spreading worldwide and infected more than 1 million people with over 690k deaths reported. It is urgent and crucial to identify COVID-19-infected patients by computed tomography (CT) accurately and rapidly. However, we found that two problems, weak supervision and lack of interpretability, hindered its development. To address these challenges, we propose an attention-based multi-flow network for COVID-19 classification and lesion localization from chest CT. In the proposed model, we built a Resnet-based multi-flow network to learn the local information and the longitudinal information from the full chest sequence slice. To assist doctors in decision-making, the attention mechanism integrated into the network, which can locate the key slices and key parts from a full chest CT sequence of patients. We have systematically evaluated our method on the CT images of 1031 cases, including 420 COVID-19 cases, 311CAP cases, and 300 non-pneumonia cases. Our method could obtain an average accuracy of 82.3%, with 85.7% sensitivity and 86.4 % specificity, which outperformed previous works.