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A Mask-guided Attention Deep Learning Model for COVID-19 Diagnosis based on an Integrated CT Scan Images Database
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  • Maede Maftouni ,
  • Bo Shen ,
  • Andrew Chung Chee Law ,
  • Niloofar Ayoobi Yazdi ,
  • Zhenyu Kong
Maede Maftouni
Virginia Tech

Corresponding Author:[email protected]

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Andrew Chung Chee Law
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Niloofar Ayoobi Yazdi
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Zhenyu Kong
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The global extent of COVID-19 mutations and the consequent depletion of hospital resources highlighted the necessity of effective computer-assisted medical diagnosis. COVID-19 detection mediated by deep learning models can help diagnose this highly contagious disease and lower infectivity and mortality rates. Computed tomography (CT) is the preferred imaging modality for building automatic COVID-19 screening and diagnosis models. It is well-known that the training set size significantly impacts the performance and generalization of deep learning models. However, accessing a large dataset of CT scan images from an emerging disease like COVID-19 is challenging. Therefore, data efficiency becomes a significant factor in choosing a learning model. To this end, we present a multi-task learning approach, namely, a mask-guided attention (MGA) classifier, to improve the generalization and data efficiency of COVID-19 classification on lung CT scan images.
The novelty of this method is compensating for the scarcity of data by employing more supervision with lesion masks, increasing the sensitivity of the model to COVID-19 manifestations, and helping both generalization and classification performance. Our proposed model achieves better overall performance than the single-task baseline and state-of-the-art models, as measured by various popular metrics. In our experiment with different percentages of data from our curated dataset, the classification performance gain from this multi-task learning approach is more significant for the smaller training sizes. Furthermore, experimental results demonstrate that our method enhances the focus on the lesions, as witnessed by both
attention and attribution maps, resulting in a more interpretable model.
03 Apr 2023Published in IISE Transactions on Healthcare Systems Engineering volume 13 issue 2 on pages 132-149. 10.1080/24725579.2022.2142866