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Style harmonization of panoramic radiography using deep learning.

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Bibliographic Details
Title:	Style harmonization of panoramic radiography using deep learning.
Authors:	Kim HS; Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, 50-1 Yonsei-Ro, Seodaemun-Gu, Seoul, 03722, Republic of Korea.; Department of Oral and Maxillofacial Radiology, Kyung Hee University Dental Hospital, 23 Kyungheedae-ro, Dongdaemun-gu, Seoul, 02447, Republic of Korea., Seol J; Graduate School of Artificial Intelligence, Ulsan National Institute of Science and Technology, 50 UNIST-Gil, Ulsan, 44919, Republic of Korea., Lee JY; Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, 50-1 Yonsei-Ro, Seodaemun-Gu, Seoul, 03722, Republic of Korea., Han SS; Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, 50-1 Yonsei-Ro, Seodaemun-Gu, Seoul, 03722, Republic of Korea., Yoo J; Graduate School of Artificial Intelligence, Ulsan National Institute of Science and Technology, 50 UNIST-Gil, Ulsan, 44919, Republic of Korea. Jaejun.yoo@unist.ac.kr., Lee C; Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, 50-1 Yonsei-Ro, Seodaemun-Gu, Seoul, 03722, Republic of Korea. chenalee@yuhs.ac.
Source:	Oral radiology [Oral Radiol] 2025 Jan; Vol. 41 (1), pp. 111-119. Date of Electronic Publication: 2024 Oct 29.
Publication Type:	Journal Article; Research Support, Non-U.S. Gov't
Journal Info:	Publisher: Springer-Verlag Tokyo Country of Publication: Japan NLM ID: 8806621 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1613-9674 (Electronic) Linking ISSN: 09116028 NLM ISO Abbreviation: Oral Radiol Subsets: MEDLINE
Database:	MEDLINE Ultimate

Description
ISSN:	1613-9674
DOI:	10.1007/s11282-024-00782-2