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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.
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