  Under the global pandemic of COVID-19, building an automated framework that
quantifies the severity of COVID-19 and localizes the relevant lesion on chest
X-ray images has become increasingly important. Although pixel-level lesion
severity labels, e.g. lesion segmentation, can be the most excellent target to
build a robust model, collecting enough data with such labels is difficult due
to time and labor-intensive annotation tasks. Instead, array-based severity
labeling that assigns integer scores on six subdivisions of lungs can be an
alternative choice enabling the quick labeling. Several groups proposed deep
learning algorithms that quantify the severity of COVID-19 using the
array-based COVID-19 labels and localize the lesions with explainability maps.
To further improve the accuracy and interpretability, here we propose a novel
Vision Transformer tailored for both quantification of the severity and
clinically applicable localization of the COVID-19 related lesions. Our model
is trained in a weakly-supervised manner to generate the full probability maps
from weak array-based labels. Furthermore, a novel progressive self-training
method enables us to build a model with a small labeled dataset. The
quantitative and qualitative analysis on the external testset demonstrates that
our method shows comparable performance with radiologists for both tasks with
stability in a real-world application.
