  Chest imaging plays an essential role in diagnosing and predicting patients
with COVID-19 with evidence of worsening respiratory status. Many deep
learning-based approaches for pneumonia recognition have been developed to
enable computer-aided diagnosis. However, the long training and inference time
makes them inflexible, and the lack of interpretability reduces their
credibility in clinical medical practice. This paper aims to develop a
pneumonia recognition framework with interpretability, which can understand the
complex relationship between lung features and related diseases in chest X-ray
(CXR) images to provide high-speed analytics support for medical practice. To
reduce the computational complexity to accelerate the recognition process, a
novel multi-level self-attention mechanism within Transformer has been proposed
to accelerate convergence and emphasize the task-related feature regions.
Moreover, a practical CXR image data augmentation has been adopted to address
the scarcity of medical image data problems to boost the model's performance.
The effectiveness of the proposed method has been demonstrated on the classic
COVID-19 recognition task using the widespread pneumonia CXR image dataset. In
addition, abundant ablation experiments validate the effectiveness and
necessity of all of the components of the proposed method.
