Zobrazeno 1 - 10 of 115 pro vyhledávání: '"Seung-min Ryu"'
1
Akademický článek
Clinical validation of enhanced CT imaging for distal radius fractures through conditional Generative Adversarial Networks (cGAN).
Autor: Hyojune Kim, Seung Min Ryu, Ji-Soo Keum, Sang-Il Oh, Kyung-Nam Kim, Young Ho Shin, In-Ho Jeon, Kyoung Hwan Koh
Publikováno v: PLoS ONE, Vol 19, Iss 8, p e0308346 (2024)
Background/purposeDistal radius fractures (DRFs) account for approximately 18% of fractures in patients 65 years and older. While plain radiographs are standard, the value of high-resolution computed tomography (CT) for detailed imaging crucial for d
Externí odkaz: https://doaj.org/article/911f60e594ca4b5289b2302505c9c4dd
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2
Akademický článek
Diagnosis of osteoporotic vertebral compression fractures and fracture level detection using multitask learning with U-Net in lumbar spine lateral radiographs
Autor: Seung Min Ryu, Soyoung Lee, Miso Jang, Jung-Min Koh, Sung Jin Bae, Seong Gyu Jegal, Keewon Shin, Namkug Kim
Publikováno v: Computational and Structural Biotechnology Journal, Vol 21, Iss , Pp 3452-3458 (2023)
Recent studies of automatic diagnosis of vertebral compression fractures (VCFs) using deep learning mainly focus on segmentation and vertebral level detection in lumbar spine lateral radiographs (LSLRs). Herein, we developed a model for simultaneous
Externí odkaz: https://doaj.org/article/d78bb8ed18144f85b6851f26918f07ea
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3
Akademický článek
Screening of adolescent idiopathic scoliosis using generative adversarial network (GAN) inversion method in chest radiographs.
Autor: Jun Soo Lee, Keewon Shin, Seung Min Ryu, Seong Gyu Jegal, Woojin Lee, Min A Yoon, Gil-Sun Hong, Sanghyun Paik, Namkug Kim
Publikováno v: PLoS ONE, Vol 18, Iss 5, p e0285489 (2023)
ObjectiveConventional computer-aided diagnosis using convolutional neural networks (CNN) has limitations in detecting sensitive changes and determining accurate decision boundaries in spectral and structural diseases such as scoliosis. We devised a n
Externí odkaz: https://doaj.org/article/6b6c41cf3bb541159468a663419d6ee9
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6
Effects of Adding CO2 to Low Level H2S Containing Aqueous Environments on the Corrosion and Hydrogen Penetration Behaviors of High-Strength Steel
Autor: Jin-seong Park, Sung Jin Kim, Hye Rin Bang, Seung Min Ryu
Publikováno v: Korean Journal of Metals and Materials. 59:533-544
The effects of adding CO2 to low level H2S containing aqueous environment on the corrosion and hydrogen penetration behaviors of high-strength steel were evaluated using a range of experimental and analytical methods. The corrosion rate of the steel
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::b8413c792bf621ba6c76fe325248345e
https://doi.org/10.3365/kjmm.2021.59.8.533
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7
Akademický článek
The Effect of Indigocarmine on Improvement of the Polyp Detection Rate during Colonoscopic Examination with Hood Cap
Autor: Sang Chang Kwon, Sung Won Choi, Seong Ho Choi, Hee Seung Park, Seung Heon Lee, Bong Gun Kim, Eun Hee Seo, Mun Jang, Seung Min Ryu, Dong Hyun Kim, Young Hoon Kim, Jun Ouk Ha, Jae Seung Lee
Publikováno v: Intestinal Research, Vol 12, Iss 1, Pp 60-65 (2014)
Background/AimsHood cap-assisted chromocolonoscopy using indigocarmine is expected to improve the detection rate of colorectal polyps, especially adenomatous polyps. Therefore, aim of the present study was to evaluate the usefulness of hood cap-assis
Externí odkaz: https://doaj.org/article/3db275d92a834947b5adc04be3989294
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9
Automated landmark identification for diagnosis of the deformity using a cascade convolutional neural network (FlatNet) on weight-bearing lateral radiographs of the foot
Autor: Seung Min Ryu, Keewon Shin, Soo Wung Shin, Sun Ho Lee, Su Min Seo, Seung-uk Cheon, Seung-Ah Ryu, Jun-Sik Kim, Sunghwan Ji, Namkug Kim
Publikováno v: Computers in biology and medicine. 148
Landmark detection in flatfoot radiographs is crucial in analyzing foot deformity. Here, we evaluated the accuracy and efficiency of the automated identification of flatfoot landmarks using a newly developed cascade convolutional neural network (CNN)
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2bcf37006cab8267fbcfbc713c44c8d4
https://pubmed.ncbi.nlm.nih.gov/35961089
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Plný text Recenzováno Digitální knihovna AV ČR
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