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- Title
Modeling global and local label correlation with graph convolutional networks for multi-label chest X-ray image classification.
- Authors
Li, Lanting; Cao, Peng; Yang, Jinzhu; Zaiane, Osmar R.
- 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.
- Publication
Medical & Biological Engineering & Computing, 2022, Vol 60, Issue 9, p2567
- ISSN
0140-0118
- Publication type
Article
- DOI
10.1007/s11517-022-02604-1