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?:abstract
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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 © 2020 ACM
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