Multiscale Attention Guided Network for COVID-19 Diagnosis Using Chest X-Ray Images

Jingxiong Li, Yaqi Wang*, Shuai Wang, Jun Wang, Jun Liu, Qun Jin, Lingling Sun


研究成果: Article査読

28 被引用数 (Scopus)


Coronavirus disease 2019 (COVID-19) is one of the most destructive pandemic after millennium, forcing the world to tackle a health crisis. Automated lung infections classification using chest X-ray (CXR) images could strengthen diagnostic capability when handling COVID-19. However, classifying COVID-19 from pneumonia cases using CXR image is a difficult task because of shared spatial characteristics, high feature variation and contrast diversity between cases. Moreover, massive data collection is impractical for a newly emerged disease, which limited the performance of data thirsty deep learning models. To address these challenges, Multiscale Attention Guided deep network with Soft Distance regularization (MAG-SD) is proposed to automatically classify COVID-19 from pneumonia CXR images. In MAG-SD, MA-Net is used to produce prediction vector and attention from multiscale feature maps. To improve the robustness of trained model and relieve the shortage of training data, attention guided augmentations along with a soft distance regularization are posed, which aims at generating meaningful augmentations and reduce noise. Our multiscale attention model achieves better classification performance on our pneumonia CXR image dataset. Plentiful experiments are proposed for MAG-SD which demonstrates its unique advantage in pneumonia classification over cutting-edge models. The code is available at

ジャーナルIEEE Journal of Biomedical and Health Informatics
出版ステータスPublished - 2021 5月

ASJC Scopus subject areas

  • 健康情報管理
  • 健康情報学
  • 電子工学および電気工学
  • コンピュータ サイエンスの応用


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