Automated thorax disease diagnosis using multi-branch residual attention network.
Attention mechanism
BCE Loss
Convolutional neural network
Label smoothing
Thoracic disease diagnosis
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
24 May 2024
24 May 2024
Historique:
received:
30
01
2024
accepted:
21
05
2024
medline:
25
5
2024
pubmed:
25
5
2024
entrez:
24
5
2024
Statut:
epublish
Résumé
Chest X-ray (CXR) is an extensively utilized radiological modality for supporting the diagnosis of chest diseases. However, existing research approaches suffer from limitations in effectively integrating multi-scale CXR image features and are also hindered by imbalanced datasets. Therefore, there is a pressing need for further advancement in computer-aided diagnosis (CAD) of thoracic diseases. To tackle these challenges, we propose a multi-branch residual attention network (MBRANet) for thoracic disease diagnosis. MBRANet comprises three components. Firstly, to address the issue of inadequate extraction of spatial and positional information by the convolutional layer, a novel residual structure incorporating a coordinate attention (CA) module is proposed to extract features at multiple scales. Next, based on the concept of a Feature Pyramid Network (FPN), we perform multi-scale feature fusion in the following manner. Thirdly, we propose a novel Multi-Branch Feature Classifier (MFC) approach, which leverages the class-specific residual attention (CSRA) module for classification instead of relying solely on the fully connected layer. In addition, the designed BCEWithLabelSmoothing loss function improves the generalization ability and mitigates the problem of class imbalance by introducing a smoothing factor. We evaluated MBRANet on the ChestX-Ray14, CheXpert, MIMIC-CXR, and IU X-Ray datasets and achieved average AUCs of 0.841, 0.895, 0.805, and 0.745, respectively. Our method outperformed state-of-the-art baselines on these benchmark datasets.
Identifiants
pubmed: 38789592
doi: 10.1038/s41598-024-62813-6
pii: 10.1038/s41598-024-62813-6
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
11865Subventions
Organisme : National Natural Science Foundation of China
ID : 61672210
Organisme : Major Science and Technology Program of Henan Province
ID : 221100210500
Organisme : Henan Province Central Guided Local Science and Technology Development Funding Project
ID : Z20221343032
Informations de copyright
© 2024. The Author(s).
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