Automatic maxillary sinus segmentation and pathology classification on cone-beam computed tomographic images using deep learning
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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1 Online-Ressource.
- Sprache
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Englisch
- Erschienen in
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Automatic maxillary sinus segmentation and pathology classification on cone-beam computed tomographic images using deep learning ; volume:24 ; number:1 ; day:10 ; month:10 ; year:2024 ; pages:1-15 ; date:12.2024
BMC oral health ; 24, Heft 1 (10.10.2024), 1-15, 12.2024
- Urheber
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Altun, Oğuzhan
Özen, Duygu Çelik
Duman, Şuayip Burak
Dedeoğlu, Numan
Bayrakdar, İbrahim Şevki
Eşer, Gözde
Çelik, Özer
Sümbüllü, Muhammed Akif
Syed, Ali Zakir
- Beteiligte Personen und Organisationen
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SpringerLink (Online service)
- DOI
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10.1186/s12903-024-04924-0
- URN
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urn:nbn:de:101:1-2412272131023.069933181760
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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15.08.2025, 07:37 MESZ
Datenpartner
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Beteiligte
- Altun, Oğuzhan
- Özen, Duygu Çelik
- Duman, Şuayip Burak
- Dedeoğlu, Numan
- Bayrakdar, İbrahim Şevki
- Eşer, Gözde
- Çelik, Özer
- Sümbüllü, Muhammed Akif
- Syed, Ali Zakir
- SpringerLink (Online service)
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Cone-beam computed tomographic analysis of maxillary sinus septa among Yemeni population: a cross-sectional study
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Morphological Assessment of Maxillary Sinus Septa Variations with Cone-Beam Computed Tomography in a Turkish Population
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Buccolingual inclination of posterior dentition in maxillary impacted canine patients using quadrant analysis - A cone-beam computed tomographic study
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