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CvTGNet: A Novel Framework for Chest X-Ray Multi-label Classification

Published: 02 July 2024 Publication History

Abstract

The accurate diagnosis of multiple thoracic diseases through chest X-ray (CXR) images is a challenging yet crucial task in the medical field. Deep learning approaches have shown promise in assisting clinicians in this endeavor. In this paper, we introduce CvTGNet, a novel framework that leverages Convolutional Vision Transformer (CvT) and Graph Convolutional Network (GCN) to enhance CXR diagnosis. CvTGNet is designed to harness the super generalization capability of CvT, a hybrid architecture that combines Convolutional Neural Networks (CNNs) and Transformers. We employ GCN to explore the co-occurrence relationships among thoracic diseases, allowing the model to gain insights into intricate pathological connections that might be overlooked by traditional methods. This information guides the CvT model in making more accurate multi-label pathological classifications. We conducted extensive experiments on two prominent CXR datasets, ChestX-Ray14 and CheXpert, to evaluate the performance of CvTGNet. The results demonstrate that our proposed method consistently outperforms other state-of-the-art approaches in terms of Area Under the Receiver Operating Characteristic Curve (AUC-ROC) scores for multilabel pathological classification. We also conducted ablation experiments to understand the contribution of each component of CvTGNet. Overall, our CvTGNet framework presents a significant advancement in CXR diagnosis. By combining the strengths of CvT and CGN, we achieve remarkable accuracy in detecting multiple thoracic diseases from CXR images. This approach holds promise in enhancing medical diagnosis, enabling clinicians to make more informed decisions and improving patient outcomes.

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cover image ACM Conferences
CF '24: Proceedings of the 21st ACM International Conference on Computing Frontiers
May 2024
345 pages
ISBN:9798400705977
DOI:10.1145/3649153
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Publication History

Published: 02 July 2024

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Author Tags

  1. CvT
  2. GCN
  3. chest X-ray
  4. multi-label classification
  5. transformer

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  • Research-article
  • Research
  • Refereed limited

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  • Department of Education of Guangdong Province, China
  • Department of Education of Guangdong Province, China
  • Science, Technology and Innovation Bureau of Shenzhen Municipality, China

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CF '24
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CF '24 Paper Acceptance Rate 33 of 105 submissions, 31%;
Overall Acceptance Rate 273 of 785 submissions, 35%

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