MVC: A Multi-Task Vision Transformer Network for COVID-19 Diagnosis from Chest X-ray Images

TL;DR

This paper introduces MVC, a multi-task vision transformer model that simultaneously classifies COVID-19 from chest X-rays and identifies affected regions, improving performance over existing methods.

Contribution

The paper presents a novel multi-task vision transformer framework that unifies disease classification and affected region identification in chest X-ray analysis.

Findings

MVC outperforms baseline models in classification accuracy.

MVC effectively identifies affected regions in X-ray images.

The approach demonstrates superior results on benchmark COVID-19 datasets.

Abstract

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.