  Medical image analysis using computer-based algorithms has attracted
considerable attention from the research community and achieved tremendous
progress in the last decade. With recent advances in computing resources and
availability of large-scale medical image datasets, many deep learning models
have been developed for disease diagnosis from medical images. However,
existing techniques focus on sub-tasks, e.g., disease classification and
identification, individually, while there is a lack of a unified framework
enabling multi-task diagnosis. Inspired by the capability of Vision
Transformers in both local and global representation learning, we propose in
this paper a new method, namely Multi-task Vision Transformer (MVC) for
simultaneously classifying chest X-ray images and identifying affected regions
from the input data. Our method is built upon the Vision Transformer but
extends its learning capability in a multi-task setting. We evaluated our
proposed method and compared it with existing baselines on a benchmark dataset
of COVID-19 chest X-ray images. Experimental results verified the superiority
of the proposed method over the baselines on both the image classification and
affected region identification tasks.
