Zobrazeno 1 - 10
of 31
pro vyhledávání: '"Dong-Yul Oh"'
Publikováno v:
IEEE Access, Vol 7, Pp 32256-32265 (2019)
We present a method for digital subtraction angiography based on phase-based nonrigid deformation with specific consideration for changes between the image pairs. Input images are transformed into a scale-space representation using complex-valued fil
Externí odkaz:
https://doaj.org/article/3ece26789b604664b173c457b7d0884e
Autor:
Yejin Jeon, Kyeorye Lee, Leonard Sunwoo, Dongjun Choi, Dong Yul Oh, Kyong Joon Lee, Youngjune Kim, Jeong-Whun Kim, Se Jin Cho, Sung Hyun Baik, Roh-eul Yoo, Yun Jung Bae, Byung Se Choi, Cheolkyu Jung, Jae Hyoung Kim
Publikováno v:
Diagnostics, Vol 11, Iss 2, p 250 (2021)
Accurate image interpretation of Waters’ and Caldwell view radiographs used for sinusitis screening is challenging. Therefore, we developed a deep learning algorithm for diagnosing frontal, ethmoid, and maxillary sinusitis on both Waters’ and Cal
Externí odkaz:
https://doaj.org/article/1b8b0c610d194e57b6ce2613f4c25206
Autor:
Hae Young Kim, Kyeorye Lee, Won Chang, Youngjune Kim, Sungsoo Lee, Dong Yul Oh, Leonard Sunwoo, Yoon Jin Lee, Young Hoon Kim
Publikováno v:
Diagnostics, Vol 11, Iss 3, p 410 (2021)
The performance of deep learning algorithm (DLA) to that of radiologists was compared in detecting low contrast objects in CT phantom images under various imaging conditions. For training, 10,000 images were created using American College of Radiolog
Externí odkaz:
https://doaj.org/article/0d08dfd6a4274e938a2cd851f5f66934
Autor:
Kyeorye Lee, Kyoung Ho Lee, Jong Chul Ye, Young Hoon Kim, Eun Hee Kang, Hae Young Kim, Won Chang, Ji Hoon Park, Yoon Jin Lee, Youngjune Kim, Dong Yul Oh
Publikováno v:
European Radiology. 31:8755-8764
(1) To compare low-contrast detectability of a deep learning–based denoising algorithm (DLA) with ADMIRE and FBP, and (2) to compare image quality parameters of DLA with those of reconstruction methods from two different CT vendors (ADMIRE, IMR, an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8854ca9052f1e7c83f2d7661b1ea3efd
https://doi.org/10.1007/s00330-021-07810-3
https://doi.org/10.1007/s00330-021-07810-3
Autor:
Dong Yul Oh, Il Dong Yun
Publikováno v:
Sensors, Vol 18, Iss 5, p 1308 (2018)
Detecting an anomaly or an abnormal situation from given noise is highly useful in an environment where constantly verifying and monitoring a machine is required. As deep learning algorithms are further developed, current studies have focused on this
Externí odkaz:
https://doaj.org/article/43bc90332d4845c7a72d2272906f8d84
Publikováno v:
Imaging.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::965e94cb6441f7c5d05534430e9e3b53
https://doi.org/10.1183/13993003.congress-2021.pa1861
https://doi.org/10.1183/13993003.congress-2021.pa1861
Publikováno v:
IEEE Access, Vol 7, Pp 32256-32265 (2019)
We present a method for digital subtraction angiography based on phase-based nonrigid deformation with specific consideration for changes between the image pairs. Input images are transformed into a scale-space representation using complex-valued fil
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4ee6b307c9ae26aed731b5e2b1db0f84
https://ieeexplore.ieee.org/document/8667412/
https://ieeexplore.ieee.org/document/8667412/
Autor:
Kyeorye Lee, Sung-Soo Lee, Young Hoon Kim, Yoon Jin Lee, Leonard Sunwoo, Hae Young Kim, Won Chang, Dong Yul Oh, Youngjune Kim
Publikováno v:
Diagnostics, Vol 11, Iss 410, p 410 (2021)
Diagnostics
Volume 11
Issue 3
Diagnostics
Volume 11
Issue 3
The performance of deep learning algorithm (DLA) to that of radiologists was compared in detecting low contrast objects in CT phantom images under various imaging conditions. For training, 10,000 images were created using American College of Radiolog
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b09533271b5ffe1ff7ce04de6d2179c7
https://www.mdpi.com/2075-4418/11/3/410
https://www.mdpi.com/2075-4418/11/3/410
Autor:
Jeong Whun Kim, Se Jin Cho, Jae Hyoung Kim, Dong Yul Oh, Yejin Jeon, Kyeorye Lee, Cheolkyu Jung, Yun Jung Bae, Kyong Joon Lee, Roh Eul Yoo, Leonard Sunwoo, Sung Hyun Baik, Dongjun Choi, Young-June Kim, Byung Se Choi
Publikováno v:
Diagnostics
Volume 11
Issue 2
Diagnostics, Vol 11, Iss 250, p 250 (2021)
Volume 11
Issue 2
Diagnostics, Vol 11, Iss 250, p 250 (2021)
Accurate image interpretation of Waters’ and Caldwell view radiographs used for sinusitis screening is challenging. Therefore, we developed a deep learning algorithm for diagnosing frontal, ethmoid, and maxillary sinusitis on both Waters’ and Cal
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::557ed5dad2fb459bb48e41a5aa5afa6d
https://pubmed.ncbi.nlm.nih.gov/33562764
https://pubmed.ncbi.nlm.nih.gov/33562764
Publikováno v:
Korean Journal of Radiology
Objective To compare the effects of bone suppression imaging using deep learning (BSp-DL) based on a generative adversarial network (GAN) and bone subtraction imaging using a dual energy technique (BSt-DE) on radiologists’ performance for pulmonary
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9c32a0ae953a7fad6aa94a116bb4c4c0
https://doi.org/10.3348/kjr.2021.0146
https://doi.org/10.3348/kjr.2021.0146