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

• URL: http://arxiv.org/abs/2310.00418v1
• Date: Sat, 30 Sep 2023 15:52:18 GMT
• Title: MVC: A Multi-Task Vision Transformer Network for COVID-19 Diagnosis from Chest X-ray Images
• Authors: Huyen Tran, Duc Thanh Nguyen, John Yearwood
• Abstract summary: We propose 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.
• Score: 10.616065108433798
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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