Zobrazeno 1 - 10
of 61
pro vyhledávání: '"Gil Sun Hong"'
Autor:
Ra Gyoung Yoon, Ho Sung Kim, Gil Sun Hong, Ji Eun Park, Seung Chai Jung, Sang Joon Kim, Jeong Hoon Kim
Publikováno v:
PLoS ONE, Vol 13, Iss 9, p e0202891 (2018)
Although advanced magnetic resonance imaging (MRI) techniques provide useful information for the differential diagnosis of intra-axial mass-like lesions, the specific diagnostic role of multimodal MRI over conventional magnetic resonance imaging (CMR
Externí odkaz:
https://doaj.org/article/390252254a254a028cca2bf056e96ba9
Autor:
Sang Hyun Choi, Kyoung Won Kim, Gil Sun Hong, So Jung Lee, So Yeon Kim, Jong Seok Lee, Hyoung Jung Kim
Publikováno v:
Clinical and Molecular Hepatology, Vol 20, Iss 1, Pp 85-87 (2014)
Externí odkaz:
https://doaj.org/article/6066295b62f34433afa80da4705381d0
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-11 (2024)
Abstract Transfer learning plays a pivotal role in addressing the paucity of data, expediting training processes, and enhancing model performance. Nonetheless, the prevailing practice of transfer learning predominantly relies on pre-trained models de
Externí odkaz:
https://doaj.org/article/22a4afdf7bea47209761ce87162adc55
Diagnosis of Scoliosis Using Chest Radiographs with a Semi-Supervised Generative Adversarial Network
Autor:
Woojin Lee, Keewon Shin, Junsoo Lee, Seung-Jin Yoo, Min A Yoon, Yo Won Choi, Gil-Sun Hong, Namkug Kim, Sanghyun Paik
Publikováno v:
대한영상의학회지, Vol 83, Iss 6, Pp 1298-1311 (2022)
Purpose To develop and validate a deep learning-based screening tool for the early diagnosis of scoliosis using chest radiographs with a semi-supervised generative adversarial network (GAN). Materials and Methods Using a semi-supervised learning fr
Externí odkaz:
https://doaj.org/article/e4c1be80b8b64163a4e00c76f1918301
Autor:
Seungjun Lee, Boryeong Jeong, Minjee Kim, Ryoungwoo Jang, Wooyul Paik, Jiseon Kang, Won Jung Chung, Gil-Sun Hong, Namkug Kim
Publikováno v:
Nature Communications, Vol 13, Iss 1, Pp 1-11 (2022)
Triage is essential for the early diagnosis and reporting of emergency patients in the emergency department. Here, the authors develop an anomaly detection algorithm with a deep generative model that reprioritizes radiology worklists and provides les
Externí odkaz:
https://doaj.org/article/57d4c2ce439e4b4fb13f1c2c353248e0
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
Autor:
Kiduk Kim, Kyungjin Cho, Ryoungwoo Jang, Sunggu Kyung, Soyoung Lee, Sungwon Ham, Choi, Edward, Gil-Sun Hong, Namkug Kim
Publikováno v:
Korean Journal of Radiology; Mar2024, Vol. 25 Issue 3, p224-242, 19p
Autor:
Gil-Sun Hong, Miso Jang, Sunggu Kyung, Kyungjin Cho, Jiheon Jeong, Yoojin Lee, Grace, Keewon Shin, Ki Duk Kim, Seung Min Ryu, Joon Beom Seo, Sang Min Lee, Namkug Kim
Publikováno v:
Korean Journal of Radiology; Nov2023, Vol. 24 Issue 11, p1061-1080, 20p
Autor:
Kyungjin Cho, Ki Duk Kim, Yujin Nam, Jiheon Jeong, Jeeyoung Kim, Changyong Choi, Soyoung Lee, Jun Soo Lee, Seoyeon Woo, Gil-Sun Hong, Joon Beom Seo, Namkug Kim
Publikováno v:
Journal of Digital Imaging.
Training deep learning models on medical images heavily depends on experts’ expensive and laborious manual labels. In addition, these images, labels, and even models themselves are not widely publicly accessible and suffer from various kinds of bia
Publikováno v:
Insights into Imaging, Vol 12, Iss 1, Pp 1-11 (2021)
Insights into Imaging
Insights into Imaging
Background To investigate diagnostic errors and their association with adverse outcomes (AOs) during patient revisits with repeat imaging (RVRIs) in the emergency department (ED). Results Diagnostic errors stemming from index imaging studies and AOs