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Unsupervised Domain Adaptation for Vertebrae Detection and Identification in 3D CT Volumes Using a Domain Sanity Loss.

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第一作者： Pascal,Sager

第一单位： Centre for AI, Technikumstrasse 71, Zurich University of Applied Sciences, 8400 Winterthur, Switzerland.

作者： Pascal,Sager ^[1] ; Sebastian,Salzmann ^[1] ; Felice,Burn ^[2] ; Thilo,Stadelmann ^[3]

作者单位： Centre for AI, Technikumstrasse 71, Zurich University of Applied Sciences, 8400 Winterthur, Switzerland. ^[1] Cantonal Hospital Aarau, AI and Data Science CoE, Tellstrasse 25, 5001 Aarau, Switzerland. ^[2] Centre for AI, Technikumstrasse 71, Zurich University of Applied Sciences, 8400 Winterthur, Switzerland.;ECLT European Centre for Living Technology, 30123 Venice, Italy. ^[3]

关键词 data centrism deep learning semantic segmentation semi-supervised learning transfer learning unsupervised domain adaptation vertebrae detection vertebrae identification

DOI 10.3390/jimaging8080222

PMID 36005465

发布时间 2022-08-27

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Journal of imaging

2022年8卷8期

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Journal of imaging

2022年8卷8期

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Unsupervised Domain Adaptation for Vertebrae Detection and Identification in 3D CT Volumes Using a Domain Sanity Loss.

Journal of imaging

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Unsupervised Domain Adaptation for Vertebrae Detection and Identification in 3D CT Volumes Using a Domain Sanity Loss.

Journal of imaging

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Journal of imaging

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