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Article: AutoSegNet: An Automated Neural Network for Image Segmentation

TitleAutoSegNet: An Automated Neural Network for Image Segmentation
Authors
KeywordsImage segmentation
Computer architecture
Convolution
Optimization
Biological neural networks
Issue Date2020
PublisherInstitute of Electrical and Electronics Engineers (IEEE): OAJ. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6287639
Citation
IEEE Access, 2020, v. 8, p. 92452-92461 How to Cite?
AbstractNeural Architecture Search (NAS) has drawn significant attention as a tool for automatically constructing deep neural networks. The generated neural networks are mainly applied for image classification, and natural language processing. However, there are increasing demands for image segmentation in various areas, such as medical image processing, satellite image object location, and autopilot technology. We propose a NAS method called Automated Segmentation Network (AutoSegNet), targeting industrial and medical image segmentation. The search architectures are constructed by stacking the downsampling layer, the bridge layer, and the upsampling layer, which are explored by a recurrent neural network. Compared with other related methods for image segmentation, the proposed method has a small search space but can explore most of the-state-of-the-art supervised image segmentation models. We perform verification on two datasets, and the results show that AutoSegNet achieves superior segmentation results with clear and continuous segmented edges, as well as better image details.
Persistent Identifierhttp://hdl.handle.net/10722/287594
ISSN
2019 Impact Factor: 3.745
2015 SCImago Journal Rankings: 0.947
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorXu, Z-
dc.contributor.authorZuo, S-
dc.contributor.authorLam, EY-
dc.contributor.authorLee, B-
dc.contributor.authorChen, N-
dc.date.accessioned2020-10-05T12:00:21Z-
dc.date.available2020-10-05T12:00:21Z-
dc.date.issued2020-
dc.identifier.citationIEEE Access, 2020, v. 8, p. 92452-92461-
dc.identifier.issn2169-3536-
dc.identifier.urihttp://hdl.handle.net/10722/287594-
dc.description.abstractNeural Architecture Search (NAS) has drawn significant attention as a tool for automatically constructing deep neural networks. The generated neural networks are mainly applied for image classification, and natural language processing. However, there are increasing demands for image segmentation in various areas, such as medical image processing, satellite image object location, and autopilot technology. We propose a NAS method called Automated Segmentation Network (AutoSegNet), targeting industrial and medical image segmentation. The search architectures are constructed by stacking the downsampling layer, the bridge layer, and the upsampling layer, which are explored by a recurrent neural network. Compared with other related methods for image segmentation, the proposed method has a small search space but can explore most of the-state-of-the-art supervised image segmentation models. We perform verification on two datasets, and the results show that AutoSegNet achieves superior segmentation results with clear and continuous segmented edges, as well as better image details.-
dc.languageeng-
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE): OAJ. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6287639-
dc.relation.ispartofIEEE Access-
dc.rightsIEEE Access. Copyright © Institute of Electrical and Electronics Engineers (IEEE): OAJ.-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectImage segmentation-
dc.subjectComputer architecture-
dc.subjectConvolution-
dc.subjectOptimization-
dc.subjectBiological neural networks-
dc.titleAutoSegNet: An Automated Neural Network for Image Segmentation-
dc.typeArticle-
dc.identifier.emailLam, EY: elam@eee.hku.hk-
dc.identifier.authorityLam, EY=rp00131-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/ACCESS.2020.2995367-
dc.identifier.scopuseid_2-s2.0-85085639004-
dc.identifier.hkuros314916-
dc.identifier.volume8-
dc.identifier.spage92452-
dc.identifier.epage92461-
dc.identifier.isiWOS:000539041600015-
dc.publisher.placeUnited States-
dc.identifier.issnl2169-3536-

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