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Modeling global and local label correlation with graph convolutional networks for multi-label chest X-ray image classification.
- Source :
-
Medical & Biological Engineering & Computing . Sep2022, Vol. 60 Issue 9, p2567-2588. 22p. 2 Color Photographs, 2 Black and White Photographs, 8 Charts, 6 Graphs. - Publication Year :
- 2022
-
Abstract
- The diagnosis of chest diseases is a challenging task for assessing thousands of radiology subjects. Their diagnosis decisions heavily rely on the expert radiologists' manual annotations. It is important to develop automated analysis methods for the computer-aided diagnosis of chest diseases on chest radiography. To explore the label relationship and improve the diagnosis performance, we present an end-to-end multi-label learning framework for jointly modeling the global and local label correlation, called GL-MLL that (1) explores the label correlation from a globally static view and a locally adaptive view, (2) considers the imbalanced class distribution, and (3) focuses on capturing label-specific features in image-level representation. We validate the performance of the proposed framework on the CheXpert dataset. The results demonstrate that the proposed GL-MLL outperforms state-of-the-art approaches. The code is available at https://github.com/llt1836/GL-MLL. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 01400118
- Volume :
- 60
- Issue :
- 9
- Database :
- Academic Search Index
- Journal :
- Medical & Biological Engineering & Computing
- Publication Type :
- Academic Journal
- Accession number :
- 158447320
- Full Text :
- https://doi.org/10.1007/s11517-022-02604-1