Modeling global and local label correlation with graph convolutional networks for multi-label chest X-ray image classification

• Published in: Medical & biological engineering & computing Vol. 60; no. 9; pp. 2567 - 2588
• Main Authors: Li, Lanting, Cao, Peng, Yang, Jinzhu, Zaiane, Osmar R.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2022; Springer Nature B.V
• Subjects: Annotations; Artificial neural networks; Automation; Bioengineering; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Chest; Classification; Computer Applications; Correlation; Deep learning; Diagnosis; Disease; Engineering; Human Physiology; Image classification; Imaging; Medical diagnosis; Medical imaging; Modelling; Original Article; Patients; Pneumonia; Radiography; Radiology; Label correlation modeling; Chest X-ray interpretation; Graph convolutional networks; Multi-label classification

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 ches...