A Mask-guided Attention Deep Learning Model for COVID-19 Diagnosis based
on an Integrated CT Scan Images Database
Abstract
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.