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2024 연구성과별 연구자 정보 (532 / 2344)

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Document Title Author Full Name Author Short Name Index Corresponding Address ResearcherID ResearcherID Author Name ORCID ORCID Author Name Related Email
Deep learning algorithm for the automated detection and classification of nasal cavity mass in nasal endoscopic images Kim, Jong-Yeup Kim, JY 19 교신저자 Konyang Univ, Coll Med, Dept Biomed Informat, Daejeon, South Korea ISA-2120-2023 Kim, Jong-Yeup dryums@gmail.com;entkwon@hanmail.net;jykim@kyuh.ac.kr;
Deep learning algorithm for the automated detection and classification of nasal cavity mass in nasal endoscopic images Kwon, Jae Hwan Kwon, JH 20 교신저자 Kosin Univ, Coll Med, Dept Otolaryngol Head & Neck Surg, Busan, South Korea dryums@gmail.com;entkwon@hanmail.net;jykim@kyuh.ac.kr;
Deep learning algorithm for the automated detection and classification of nasal cavity mass in nasal endoscopic images Yu, Myeong Sang Yu, MS 21 교신저자 Univ Ulsan, Coll Med, Asan Med Ctr, Dept Otorhinolaryngol Head & Neck Surg, Seoul, South Korea dryums@gmail.com;entkwon@hanmail.net;jykim@kyuh.ac.kr;
DEEP LEARNING APPROACH FOR CLASSIFICATION OF WATER BOTTOM AND SURFACE FROM BATHYMETRIC LIDAR POINT CLOUDS Song, Ahram Song, A 1 교신저자 Kyungpook Natl Univ, Dept Locat Based Informat Syst, Sangju, South Korea
DEEP LEARNING APPROACH FOR CLASSIFICATION OF WATER BOTTOM AND SURFACE FROM BATHYMETRIC LIDAR POINT CLOUDS Kim, Hyejin Kim, H 2 Konkuk Univ, Social Ecotech Inst, Seoul, South Korea
Deep learning based highly accurate transplanted bioengineered corneal equivalent thickness measurement using optical coherence tomography Seong, Daewoon Seong, D 1 Kyungpook Natl Univ, Coll IT Engn, Sch Elect & Elect Engn, Daegu, South Korea okeye@knu.ac.kr;msjeon@knu.ac.kr;
Deep learning based highly accurate transplanted bioengineered corneal equivalent thickness measurement using optical coherence tomography Lee, Euimin Lee, E 2 Kyungpook Natl Univ, Coll IT Engn, Sch Elect & Elect Engn, Daegu, South Korea okeye@knu.ac.kr;msjeon@knu.ac.kr;
Deep learning based highly accurate transplanted bioengineered corneal equivalent thickness measurement using optical coherence tomography Kim, Yoonseok Kim, Y 3 Kyungpook Natl Univ, Coll IT Engn, Sch Elect & Elect Engn, Daegu, South Korea okeye@knu.ac.kr;msjeon@knu.ac.kr;
Deep learning based highly accurate transplanted bioengineered corneal equivalent thickness measurement using optical coherence tomography Yae, Che Gyem Yae, CG 4 Kyungpook Natl Univ Hosp, Biomed Inst, Daegu, South Korea okeye@knu.ac.kr;msjeon@knu.ac.kr;
Deep learning based highly accurate transplanted bioengineered corneal equivalent thickness measurement using optical coherence tomography Yae, Che Gyem Yae, CG 4 Kyungpook Natl Univ, Sch Med, Dept Ophthalmol, Daegu, South Korea okeye@knu.ac.kr;msjeon@knu.ac.kr;
Deep learning based highly accurate transplanted bioengineered corneal equivalent thickness measurement using optical coherence tomography Choi, JeongMun Choi, J 5 Kyungpook Natl Univ Hosp, Biomed Inst, Daegu, South Korea okeye@knu.ac.kr;msjeon@knu.ac.kr;
Deep learning based highly accurate transplanted bioengineered corneal equivalent thickness measurement using optical coherence tomography Choi, JeongMun Choi, J 5 Kyungpook Natl Univ, Sch Med, Dept Ophthalmol, Daegu, South Korea okeye@knu.ac.kr;msjeon@knu.ac.kr;
Deep learning based highly accurate transplanted bioengineered corneal equivalent thickness measurement using optical coherence tomography Kim, Hong Kyun Kim, HK 6 교신저자 Kyungpook Natl Univ Hosp, Biomed Inst, Daegu, South Korea ITT-7758-2023 Kim, Hong Kyun okeye@knu.ac.kr;msjeon@knu.ac.kr;
Deep learning based highly accurate transplanted bioengineered corneal equivalent thickness measurement using optical coherence tomography Kim, Hong Kyun Kim, HK 6 교신저자 Kyungpook Natl Univ, Sch Med, Dept Ophthalmol, Daegu, South Korea ITT-7758-2023 Kim, Hong Kyun okeye@knu.ac.kr;msjeon@knu.ac.kr;
Deep learning based highly accurate transplanted bioengineered corneal equivalent thickness measurement using optical coherence tomography Jeon, Mansik Jeon, M 7 교신저자 Kyungpook Natl Univ, Coll IT Engn, Sch Elect & Elect Engn, Daegu, South Korea okeye@knu.ac.kr;msjeon@knu.ac.kr;
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