  Developing a robust algorithm to diagnose and quantify the severity of
COVID-19 using Chest X-ray (CXR) requires a large number of well-curated
COVID-19 datasets, which is difficult to collect under the global COVID-19
pandemic. On the other hand, CXR data with other findings are abundant. This
situation is ideally suited for the Vision Transformer (ViT) architecture,
where a lot of unlabeled data can be used through structural modeling by the
self-attention mechanism. However, the use of existing ViT is not optimal,
since feature embedding through direct patch flattening or ResNet backbone in
the standard ViT is not intended for CXR. To address this problem, here we
propose a novel Vision Transformer that utilizes low-level CXR feature corpus
obtained from a backbone network that extracts common CXR findings.
Specifically, the backbone network is first trained with large public datasets
to detect common abnormal findings such as consolidation, opacity, edema, etc.
Then, the embedded features from the backbone network are used as corpora for a
Transformer model for the diagnosis and the severity quantification of
COVID-19. We evaluate our model on various external test datasets from totally
different institutions to evaluate the generalization capability. The
experimental results confirm that our model can achieve the state-of-the-art
performance in both diagnosis and severity quantification tasks with superior
generalization capability, which are sine qua non of widespread deployment.
