Zobrazeno 1 - 10
of 21
pro vyhledávání: '"Beomhee Park"'
Autor:
Michela Antonelli, Annika Reinke, Spyridon Bakas, Keyvan Farahani, Annette Kopp-Schneider, Bennett A. Landman, Geert Litjens, Bjoern Menze, Olaf Ronneberger, Ronald M. Summers, Bram van Ginneken, Michel Bilello, Patrick Bilic, Patrick F. Christ, Richard K. G. Do, Marc J. Gollub, Stephan H. Heckers, Henkjan Huisman, William R. Jarnagin, Maureen K. McHugo, Sandy Napel, Jennifer S. Golia Pernicka, Kawal Rhode, Catalina Tobon-Gomez, Eugene Vorontsov, James A. Meakin, Sebastien Ourselin, Manuel Wiesenfarth, Pablo Arbeláez, Byeonguk Bae, Sihong Chen, Laura Daza, Jianjiang Feng, Baochun He, Fabian Isensee, Yuanfeng Ji, Fucang Jia, Ildoo Kim, Klaus Maier-Hein, Dorit Merhof, Akshay Pai, Beomhee Park, Mathias Perslev, Ramin Rezaiifar, Oliver Rippel, Ignacio Sarasua, Wei Shen, Jaemin Son, Christian Wachinger, Liansheng Wang, Yan Wang, Yingda Xia, Daguang Xu, Zhanwei Xu, Yefeng Zheng, Amber L. Simpson, Lena Maier-Hein, M. Jorge Cardoso
Publikováno v:
Nature Communications, Vol 13, Iss 1, Pp 1-13 (2022)
International challenges have become the de facto standard for comparative assessment of image analysis algorithms. Here, the authors present the results of a biomedical image segmentation challenge, showing that a method capable of performing well o
Externí odkaz:
https://doaj.org/article/b2b75489b22a48efa4cd6eef2bea9d67
Autor:
Taehun Kim, Kyung Hwa Lee, Sungwon Ham, Beomhee Park, Sangwook Lee, Dayeong Hong, Guk Bae Kim, Yoon Soo Kyung, Choung-Soo Kim, Namkug Kim
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-1 (2022)
Externí odkaz:
https://doaj.org/article/e9ec4f78e95b49b4a1e6455c94cae931
Publikováno v:
Journal of Digital Imaging. 35:1061-1068
Algorithms that automatically identify nodular patterns in chest X-ray (CXR) images could benefit radiologists by reducing reading time and improving accuracy. A promising approach is to use deep learning, where a deep neural network (DNN) is trained
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9fbf8b951ecaa9069addd173dbb450a9
https://doi.org/10.1007/s10278-022-00608-9
https://doi.org/10.1007/s10278-022-00608-9
Autor:
Joon Beom Seo, Sang Min Lee, Young-Hoon Cho, Beomhee Park, Namkug Kim, Yongwon Cho, Gaeun Lee, June-Goo Lee
Publikováno v:
International Journal of Imaging Systems and Technology. 31:72-81
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5507e7e8fc86e7ec1b731966f055ce95
https://doi.org/10.1002/ima.22508
https://doi.org/10.1002/ima.22508
Autor:
Namkug Kim, Han Chu Lee, Yung Sang Lee, Kang Mo Kim, Danbi Lee, Jihye Yun, Gwang Hyeon Choi, Beomhee Park, Young Hwa Chung, Jonggi Choi, Ju Hyun Shim
Publikováno v:
Scientific Reports, Vol 10, Iss 1, Pp 1-10 (2020)
Scientific Reports
Scientific Reports
There is a significant discrepancy between the actual choice for initial treatment option for hepatocellular carcinoma (HCC) and recommendations from the currently used BCLC staging system. We develop a machine learning-based clinical decision suppor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ea1170ce8c39be3c983a19af934ef4fc
http://link.springer.com/article/10.1038/s41598-020-71796-z
http://link.springer.com/article/10.1038/s41598-020-71796-z
Autor:
Mijeong Song, Joon Beom Seo, Jongha Park, Beomhee Park, Yongwon Cho, Minho Lee, Hee Jun Park, Jihye Yun, Namkug Kim
Publikováno v:
J Digit Imaging
Lung lobe segmentation in chest CT has been used for the analysis of lung functions and surgical planning. However, accurate lobe segmentation is difficult as 80% of patients have incomplete and/or fake fissures. Furthermore, lung diseases such as ch
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fb6b0a1881214199abc975bddb9d7c9e
https://doi.org/10.1007/s10278-019-00223-1
https://doi.org/10.1007/s10278-019-00223-1
Autor:
Yongwon Cho, Areum Lee, Heounjeong Go, Hyunna Lee, Beomhee Park, Young-Gon Kim, Namkug Kim, Gyuheon Choi
Publikováno v:
Scientific Reports, Vol 9, Iss 1, Pp 1-10 (2019)
Scientific Reports
Scientific Reports
Pathologic diagnoses mainly depend on visual scoring by pathologists, a process that can be time-consuming, laborious, and susceptible to inter- and/or intra-observer variations. This study proposes a novel method to enhance pathologic scoring of ren
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b223b3604b6730425a97315b5fba4c31
http://link.springer.com/article/10.1038/s41598-019-41479-5
http://link.springer.com/article/10.1038/s41598-019-41479-5
Publikováno v:
Computers in biology and medicine. 136
Background and objective It is important to alleviate annotation efforts and costs by efficiently training on medical images. We performed a stress test on several strong labels for curriculum learning with a convolutional neural network to different
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ec97cd3ef6f063f1fa1bfeae7cccef17
https://pubmed.ncbi.nlm.nih.gov/34392128
https://pubmed.ncbi.nlm.nih.gov/34392128
Publikováno v:
IEEE transactions on bio-medical engineering. 68(10)
Intraoral functions are results of complex and well-orchestrated sensorimotor loop operations, and therefore vulnerable to small functional or neural defects. To secure the vital intraoral functions, it is important to find a way to favorably interve
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c0ae1719bfaac95e27369b26504c49be
https://pubmed.ncbi.nlm.nih.gov/33819145
https://pubmed.ncbi.nlm.nih.gov/33819145
Autor:
Sang Min Lee, Jinkyeong Sung, Eunkyung Jung, Kyu-Hwan Jung, Sohee Park, Woong Bae, Beomhee Park, Joon Beom Seo
Publikováno v:
Radiology. 299(2)
Background Previous studies assessing the effects of computer-aided detection on observer performance in the reading of chest radiographs used a sequential reading design that may have biased the results because of reading order or recall bias. Purpo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8f5269b6bd79450f0fbe0a5536b5ad3d
https://pubmed.ncbi.nlm.nih.gov/33754828
https://pubmed.ncbi.nlm.nih.gov/33754828