Suchergebnisse - "multi-class image segmentation"
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Autoren: et al.
Weitere Verfasser: et al.
Quelle: IEEE Trans Med Imaging
IEEE Transactions on Medical ImagingSchlagwörter: information engineering, fetal brain MRI, Image and Video Processing (eess.IV), FOS: Electrical engineering, electronic engineering, information engineering, deep learning, domain generalization, [INFO] Computer Science [cs], Electrical Engineering and Systems Science - Image and Video Processing, electronic engineering, multi-class image segmentation, Article, FOS: Electrical engineering
Dateibeschreibung: Print-Electronic
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Autoren: et al.
Quelle: International Journal of Mathematical, Engineering and Management Sciences, Vol 9, Iss 6, Pp 1510-1530 (2024)
Schlagwörter: 0301 basic medicine, Technology, 03 medical and health sciences, underwater image, underwater noise, QA1-939, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, fish4knowledge, multi-class image segmentation, Mathematics, encoder-decoder model
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Autoren: et al.
Weitere Verfasser: et al.
Quelle: Computers in Biology and Medicine. 197:111024
Schlagwörter: Multi-class image segmentation, [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], FOS: Computer and information sciences, [SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/Imaging, Multiple expert annotations, Computer Vision and Pattern Recognition (cs.CV), Calibration, Uncertainty, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, Computer Vision and Pattern Recognition, abdominal CT, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Dateibeschreibung: application/pdf
Zugangs-URL: http://arxiv.org/abs/2505.08685
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Autoren: et al.
Schlagwörter: Multi-class image segmentation, Super-resolution reconstructions, Congenital disorders, Fetal brain MRI
Dateibeschreibung: application/pdf
Zugangs-URL: http://hdl.handle.net/11110/2744
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Autoren: et al.
Quelle: Medical image analysis, vol. 88, pp. 102833
Schlagwörter: Pregnancy, Female, Humans, Image Processing, Computer-Assisted/methods, Brain/diagnostic imaging, Head, Fetus/diagnostic imaging, White Matter, Algorithms, Magnetic Resonance Imaging/methods, Congenital disorders, Fetal brain MRI, Multi-class image segmentation, Super-resolution reconstructions
Dateibeschreibung: application/pdf
Relation: info:eu-repo/semantics/altIdentifier/pmid/37267773; info:eu-repo/semantics/altIdentifier/eissn/1361-8423; info:eu-repo/semantics/altIdentifier/urn/urn:nbn:ch:serval-BIB_6B600CC844F94; https://serval.unil.ch/notice/serval:BIB_6B600CC844F9; https://serval.unil.ch/resource/serval:BIB_6B600CC844F9.P001/REF.pdf
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Autoren: Slunsky, Tomas
Schlagwörter: deep learning, convolutional neural network, multi-class image segmentation
Dateibeschreibung: text; 325-329; application/pdf
Relation: Proceedings I of the 26st Conference STUDENT EEICT 2020: General papers; https://conf.feec.vutbr.cz/eeict/EEICT2020; http://hdl.handle.net/11012/200588
Verfügbarkeit: http://hdl.handle.net/11012/200588
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Autoren: Slunsky, Tomas
Schlagwörter: deep learning, convolutional neural network, multi-class image segmentation
Dateibeschreibung: text; application/pdf
Zugangs-URL: http://hdl.handle.net/11012/200588
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Autoren: et al.
Quelle: International Journal of Computer Vision. 80:300-316
Schlagwörter: Image pixels, 02 engineering and technology, Keywords: Digital image storage, 12. Responsible consumption, 03 medical and health sciences, Classification processes, Segmentation, 0302 clinical medicine, Image processing, 11. Sustainability, 0202 electrical engineering, electronic engineering, information engineering, Global features, Computational challenges, Current state, Image segmentation, 4. Education, 15. Life on land, Global informations, Multi-class image segmentation, Local features, Appearance-based, 13. Climate action, Maps, 8. Economic growth, Rel Multi-class image segmentation, Relative location
Zugangs-URL: http://ai.stanford.edu/~koller/Papers/Gould+al:IJCV08.pdf
http://robotics.stanford.edu/users/koller/Papers/Gould+al:IJCV08.pdf
https://link.springer.com/article/10.1007/s11263-008-0140-x
https://core.ac.uk/display/22818096
http://ai.stanford.edu/users/koller/Papers/Gould+al:IJCV08.pdf
https://rd.springer.com/article/10.1007/s11263-008-0140-x
http://users.cecs.anu.edu.au/~sgould/papers/ijcv08-segmentation .pdf -
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Autoren: Slunský, Tomáš
Thesis Advisors: Uher, Václav, Kolařík, Martin
Schlagwörter: segmentace obrazu, image segmentation, strojové učení, konvoluční neuronové sítě, umělá inteligence, hluboké učení, machine learning, artificial intelligence, deep learning, vícetřídá segmentace obrazu, multi-class image segmentation, neuronová síť, neural network, convolutional neural networks
Verfügbarkeit: http://www.nusl.cz/ntk/nusl-400891
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Autoren: Shafei, Behrang
Schlagwörter: ddc:510, fiber reinforced silicon carbide, total variation, RKHS, graph p-Laplacian, multifilament superconductor, multi-class image segmentation, alternating optimization
Dateibeschreibung: application/pdf
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Autoren:
Weitere Verfasser:
Schlagwörter: hluboké učení, konvoluční neuronové sítě, segmentace obrazu, strojové učení, neuronová síť, umělá inteligence, vícetřídá segmentace obrazu, deep learning, convolutional neural networks, image segmentation, machine learning, neural network, artificial intelligence, multi-class image segmentation
Dateibeschreibung: application/pdf; application/zip; text/html
Relation: SLUNSKÝ, T. Vícetřídá segmentace 3D lékařských dat pomocí hlubokého učení [online]. Brno: Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. 2019.; 118177; http://hdl.handle.net/11012/177588
Verfügbarkeit: http://hdl.handle.net/11012/177588
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Weitere Verfasser:
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Autoren:
Weitere Verfasser:
Schlagwörter: strojové učení, neuronová síť, neural network, deep learning, umělá inteligence, artificial intelligence, konvoluční neuronové sítě, segmentace obrazu, machine learning, vícetřídá segmentace obrazu, hluboké učení, convolutional neural networks, image segmentation, multi-class image segmentation
Dateibeschreibung: application/pdf; application/zip; text/html
Zugangs-URL: http://hdl.handle.net/11012/177588
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Autoren:
Index Begriffe: Multi-class image segmentation, Fetal brain MRI, Congenital disorders, Super-resolution reconstructions, Article
URL:
http://repository.hkust.edu.hk/ir/Record/1783.1-128270 https://doi.org/10.1016/j.media.2023.102833 http://lbdiscover.ust.hk/uresolver?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rfr_id=info:sid/HKUST:SPI&rft.genre=article&rft.issn=13618415&rft.volume=88&rft.issue=&rft.date=2023&rft.spage=&rft.aulast=&rft.aufirst=&rft.atitle=Fetal%20brain%20tissue%20annotation%20and%20segmentation%20challenge%20results&rft.title=Medical%20Image%20Analysis http://www.scopus.com/record/display.url?eid=2-s2.0-85160611142&origin=inward http://gateway.isiknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=LinksAMR&SrcApp=PARTNER_APP&DestLinkType=FullRecord&DestApp=WOS&KeyUT=001013162700001 -
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Autoren:
Quelle: Medical Image Analysis; 10.1016/j.media.2023.102833; Medical Image Analysis. 88
Index Begriffe: Computer Graphics and Computer-Aided Design,Computer Science, Artificial Intelligence,Computer Science, Interdisciplinary Applications,Computer Vision and Pattern Recognition,Engineering, Biomedical,Health Informatics,Radiological and Ultrasound Technology,Radiology, Nuclear Medicine & Medical Imaging,Radiology, Nuclear Medicine and Imagin, Super-resolution reconstructions, Multi-class image segmentation, Mri, Fetal brain mri, Congenital disorders, super-resolution reconstructions, myelomeningocele, fetuses, fetal brain mri, congenital disorders, atlas, Radiology, nuclear medicine and imaging, Radiology, nuclear medicine & medical imaging, Radiological and ultrasound technology, Materiais, Health informatics, Engineering, biomedical, Engenharias iv, Computer vision and pattern recognition, Computer science, interdisciplinary applications, Computer science, artificial intelligence, Computer graphics and computer-aided design, Ciência da computação, Journal Publications
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Index Begriffe: deep learning, convolutional neural network, multi-class image segmentation
URL:
http://hdl.handle.net/11012/200588 https://conf.feec.vutbr.cz/eeict/EEICT2020
Proceedings I of the 26st Conference STUDENT EEICT 2020: General papershttps://conf.feec.vutbr.cz/eeict/EEICT2020 -
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Index Begriffe: deep learning, convolutional neural network, multi-class image segmentation
URL:
http://hdl.handle.net/11012/200588 https://conf.feec.vutbr.cz/eeict/EEICT2020
Proceedings I of the 26st Conference STUDENT EEICT 2020: General papershttps://conf.feec.vutbr.cz/eeict/EEICT2020 -
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Index Begriffe: deep learning, convolutional neural network, multi-class image segmentation
URL:
http://hdl.handle.net/11012/200588 https://conf.feec.vutbr.cz/eeict/EEICT2020
Proceedings I of the 26st Conference STUDENT EEICT 2020: General papershttps://conf.feec.vutbr.cz/eeict/EEICT2020 -
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Autoren: et al.
Quelle: Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society [Comput Med Imaging Graph] 2021 Mar; Vol. 88, pp. 101866. Date of Electronic Publication: 2021 Jan 12.
Publikationsart: Journal Article; Research Support, N.I.H., Extramural
Info zur Zeitschrift: Publisher: Elsevier Science Country of Publication: United States NLM ID: 8806104 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-0771 (Electronic) Linking ISSN: 08956111 NLM ISO Abbreviation: Comput Med Imaging Graph Subsets: MEDLINE
MeSH-Schlagworte: Breast Neoplasms*/diagnostic imaging , Neural Networks, Computer*, Breast ; Female ; Humans
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Autoren:
Index Begriffe: hluboké učení, konvoluční neuronové sítě, segmentace obrazu, strojové učení, neuronová síť, umělá inteligence, vícetřídá segmentace obrazu, deep learning, convolutional neural networks, image segmentation, machine learning, neural network, artificial intelligence, multi-class image segmentation, Text
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