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An Attention Mechanism using Multiple Knowledge Sources for COVID-19 Detection from CT Images

Ho Minh Duy Nguyen; Duy M. Nguyen; Huong Vu; Binh T. Nguyen; Fabrizio Nunnari; Daniel Sonntag
In: The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21). AAAI Conference on Artificial Intelligence (AAAI), Workshop on Trustworthy AI for Healthcare, February 2-9, Vancouver, BC, Canada, AAAI, 2021.


Besides principal polymerase chain reaction (PCR) tests, automatically identifying positive samples based on computed tomography (CT) scans can present a promising option in the early diagnosis of COVID-19. Recently, there have been increasing efforts to utilize deep networks for COVID-19 diagnosis based on CT scans. While these approaches mostly focus on introducing novel architectures, transfer learning techniques or construction of large scale data, we propose a novel strategy to improve several performance baselines by leveraging multiple useful information sources relevant to doctors' judgments. Specifically, infected regions and heat-map features extracted from learned networks are integrated with the global image via an attention mechanism during the learning process. This procedure makes our system more robust to noise and guides the network focusing on local lesion areas. Extensive experiments illustrate the superior performance of our approach compared to recent baselines. Furthermore, our learned network guidance presents an explainable feature to doctors to understand the connection between input and output in a grey-box model.