Discriminative Feature Learning for Thorax Disease Classification in Chest X-ray Images.

Guan, Q; Huang, Y; Luo, Y; Liu, P; Xu, M; Yang, Y

Discriminative Feature Learning for Thorax Disease Classification in Chest X-ray Images.

Guan, Q Huang, Y Luo, Y Liu, P Xu, M Yang, Y

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Trans Image Process, 2021, 30, pp. 2476-2487
Issue Date:: 2021

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Field	Value	Language
dc.contributor.author	Guan, Q
dc.contributor.author	Huang, Y
dc.contributor.author	Luo, Y
dc.contributor.author	Liu, P
dc.contributor.author	Xu, M
dc.contributor.author	Yang, Y https://orcid.org/0000-0002-0512-880X
dc.date.accessioned	2022-04-15T22:44:12Z
dc.date.available	2022-04-15T22:44:12Z
dc.date.issued	2021
dc.identifier.citation	IEEE Trans Image Process, 2021, 30, pp. 2476-2487
dc.identifier.issn	1057-7149
dc.identifier.issn	1941-0042
dc.identifier.uri	http://hdl.handle.net/10453/156298
dc.description.abstract	This paper focuses on the thorax disease classification problem in chest X-ray (CXR) images. Different from the generic image classification task, a robust and stable CXR image analysis system should consider the unique characteristics of CXR images. Particularly, it should be able to: 1) automatically focus on the disease-critical regions, which usually are of small sizes; 2) adaptively capture the intrinsic relationships among different disease features and utilize them to boost the multi-label disease recognition rates jointly. In this paper, we propose to learn discriminative features with a two-branch architecture, named ConsultNet, to achieve those two purposes simultaneously. ConsultNet consists of two components. First, an information bottleneck constrained feature selector extracts critical disease-specific features according to the feature importance. Second, a spatial-and-channel encoding based feature integrator enhances the latent semantic dependencies in the feature space. ConsultNet fuses these discriminative features to improve the performance of thorax disease classification in CXRs. Experiments conducted on the ChestX-ray14 and CheXpert dataset demonstrate the effectiveness of the proposed method.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Trans Image Process
dc.relation.isbasedon	10.1109/TIP.2021.3052711
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering, 1702 Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.mesh	Deep Learning
dc.subject.mesh	Humans
dc.subject.mesh	Lung Diseases
dc.subject.mesh	Radiographic Image Interpretation, Computer-Assisted
dc.subject.mesh	Radiography, Thoracic
dc.subject.mesh	Humans
dc.subject.mesh	Lung Diseases
dc.subject.mesh	Radiographic Image Interpretation, Computer-Assisted
dc.subject.mesh	Radiography, Thoracic
dc.subject.mesh	Deep Learning
dc.title	Discriminative Feature Learning for Thorax Disease Classification in Chest X-ray Images.
dc.type	Journal Article
utslib.citation.volume	30
utslib.location.activity	United States
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1702 Cognitive Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	closed_access	*
dc.date.updated	2022-04-15T22:44:09Z
pubs.publication-status	Published
pubs.volume	30

Abstract:

This paper focuses on the thorax disease classification problem in chest X-ray (CXR) images. Different from the generic image classification task, a robust and stable CXR image analysis system should consider the unique characteristics of CXR images. Particularly, it should be able to: 1) automatically focus on the disease-critical regions, which usually are of small sizes; 2) adaptively capture the intrinsic relationships among different disease features and utilize them to boost the multi-label disease recognition rates jointly. In this paper, we propose to learn discriminative features with a two-branch architecture, named ConsultNet, to achieve those two purposes simultaneously. ConsultNet consists of two components. First, an information bottleneck constrained feature selector extracts critical disease-specific features according to the feature importance. Second, a spatial-and-channel encoding based feature integrator enhances the latent semantic dependencies in the feature space. ConsultNet fuses these discriminative features to improve the performance of thorax disease classification in CXRs. Experiments conducted on the ChestX-ray14 and CheXpert dataset demonstrate the effectiveness of the proposed method.

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http://hdl.handle.net/10453/156298