EXAM: An Explainable Attention-based Model for COVID-19 Automatic Diagnosis

Abstract

The ongoing coronavirus disease 2019 (COVID-19) is still rapidly spreading and has caused over 7,000,000 infection cases and 400,000 deaths around the world. To come up with a fast and reliable COVID-19 diagnosis system, people seek help from machine learning area to establish computer-aided diagnosis systems with the aid of the radiological imaging techniques, like X-ray imaging and computed tomography imaging. Although artificial intelligence based architectures have achieved great improvements in performance, most of the models are still seemed as a black box to researchers. In this paper, we propose an Explainable Attention-based Model (EXAM) for COVID-19 automatic diagnosis with convincing visual interpretation. We transform the diagnosis process with radiological images into an image classification problem differentiating COVID-19, normal and community-acquired pneumonia (CAP) cases. Combining channel-wise and spatial-wise attention mechanism, the proposed approach can effectively extract key features and suppress irrelevant information. Experiment results and visualization indicate that EXAM outperforms recent state-of-art models and demonstrate its interpretability.