Multicenter Computer-Aided Diagnosis for Lymph Nodes Using Unsupervised Domain-Adaptation Networks Based on Cross-Domain Confounding Representations
Joint Authors
Shi, Dapeng
Jiang, Lingyun
Chen, Jian
Qin, RuoXi
Zhang, Huike
Qiao, Kai
Hai, Jinjin
Xu, Junling
Yan, Bin
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-01-24
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
To achieve the robust high-performance computer-aided diagnosis systems for lymph nodes, CT images may be typically collected from multicenter data, which cause the isolated performance of the model based on different data source centers.
The variability adaptation problem of lymph node data which is related to the problem of domain adaptation in deep learning differs from the general domain adaptation problem because of the typically larger CT image size and more complex data distributions.
Therefore, domain adaptation for this problem needs to consider the shared feature representation and even the conditioning information of each domain so that the adaptation network can capture significant discriminative representations in a domain-invariant space.
This paper extracts domain-invariant features based on a cross-domain confounding representation and proposes a cycle-consistency learning framework to encourage the network to preserve class-conditioning information through cross-domain image translations.
Compared with the performance of different domain adaptation methods, the accurate rate of our method achieves at least 4.4% points higher under multicenter lymph node data.
The pixel-level cross-domain image mapping and the semantic-level cycle consistency provided a stable confounding representation with class-conditioning information to achieve effective domain adaptation under complex feature distribution.
American Psychological Association (APA)
Qin, RuoXi& Zhang, Huike& Jiang, Lingyun& Qiao, Kai& Hai, Jinjin& Chen, Jian…[et al.]. 2020. Multicenter Computer-Aided Diagnosis for Lymph Nodes Using Unsupervised Domain-Adaptation Networks Based on Cross-Domain Confounding Representations. Computational and Mathematical Methods in Medicine،Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1139410
Modern Language Association (MLA)
Qin, RuoXi…[et al.]. Multicenter Computer-Aided Diagnosis for Lymph Nodes Using Unsupervised Domain-Adaptation Networks Based on Cross-Domain Confounding Representations. Computational and Mathematical Methods in Medicine No. 2020 (2020), pp.1-10.
https://search.emarefa.net/detail/BIM-1139410
American Medical Association (AMA)
Qin, RuoXi& Zhang, Huike& Jiang, Lingyun& Qiao, Kai& Hai, Jinjin& Chen, Jian…[et al.]. Multicenter Computer-Aided Diagnosis for Lymph Nodes Using Unsupervised Domain-Adaptation Networks Based on Cross-Domain Confounding Representations. Computational and Mathematical Methods in Medicine. 2020. Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1139410
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1139410