  Computed tomography (CT) imaging is a promising approach to diagnosing the
COVID-19. Machine learning methods can be employed to train models from labeled
CT images and predict whether a case is positive or negative. However, there
exists no publicly-available and large-scale CT data to train accurate models.
In this work, we propose a multi-stage attentive transfer learning framework
for improving COVID-19 diagnosis. Our proposed framework consists of three
stages to train accurate diagnosis models through learning knowledge from
multiple source tasks and data of different domains. Importantly, we propose a
novel self-supervised learning method to learn multi-scale representations for
lung CT images. Our method captures semantic information from the whole lung
and highlights the functionality of each lung region for better representation
learning. The method is then integrated to the last stage of the proposed
transfer learning framework to reuse the complex patterns learned from the same
CT images. We use a base model integrating self-attention (ATTNs) and
convolutional operations. Experimental results show that networks with ATTNs
induce greater performance improvement through transfer learning than networks
without ATTNs. This indicates attention exhibits higher transferability than
convolution. Our results also show that the proposed self-supervised learning
method outperforms several baseline methods.
