Modeling global and local label correlation with graph convolutional networks for multi-label chest X-ray image classification.
Med Biol Eng Comput
; 60(9): 2567-2588, 2022 Sep.
Article
en En
| MEDLINE
| ID: mdl-35781585
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.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Diagnóstico por Computador
Tipo de estudio:
Diagnostic_studies
/
Guideline
/
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Med Biol Eng Comput
Año:
2022
Tipo del documento:
Article
País de afiliación:
China