Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Ju Hyuck Han"'
Publikováno v:
Sensors, Vol 24, Iss 14, p 4684 (2024)
This paper presents a classification model for eye diseases utilizing attention mechanisms to learn features from fundus images and structures. The study focuses on diagnosing glaucoma by extracting retinal vessels and the optic disc from fundus imag
Externí odkaz:
https://doaj.org/article/0ac71cf055824be5b714cbac06b1f46f
Autor:
Kwang Nam Jin, Eun Young Kim, Young Jae Kim, Gi Pyo Lee, Hyungjin Kim, Sohee Oh, Yong Suk Kim, Ju Hyuck Han, Young Jun Cho
Publikováno v:
European Radiology. 32:3469-3479
Objectives We aim ed to evaluate a commercial artificial intelligence (AI) solution on a multicenter cohort of chest radiographs and to compare physicians' ability to detect and localize referable thoracic abnormalities with and without AI assistance
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::aff6c38d08932c494f65a78f694df514
https://doi.org/10.1007/s00330-021-08397-5
https://doi.org/10.1007/s00330-021-08397-5
Autor:
Ju-Hyuck, Han, Yong-Suk, Kim
Publikováno v:
International Journal of Intelligent Systems and Applications in Engineering; Vol. 11 No. 4s (2023); 165-173
In this paper, an artificial intelligence model for predicting white blood cell counts that required conventional blood tests were studied using deep learning. White blood cell counts are crucial human information for knowing inflammatory levels in t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=issn21476799::ed71ea2fb6b546c688718266ebcd1a97
https://www.ijisae.org/index.php/IJISAE/article/view/2584
https://www.ijisae.org/index.php/IJISAE/article/view/2584
Autor:
Hyun Woo Lee, Kwang Nam Jin, Sohee Oh, Sung-Yoon Kang, Sang Min Lee, In Beom Jeong, Ji Woong Son, Ju Hyuck Han, Eun Young Heo, Jung Gyu Lee, Young Jae Kim, Eun Young Kim, Young Jun Cho
Publikováno v:
Annals of the American Thoracic Society.
Artificial intelligence (AI)-assisted diagnosis imparts a high accuracy to chest radiography (CXR) interpretation; however, its benefit for non-radiologist physicians in detecting lung lesions on CXR remains unclear.To investigate whether AI assistan
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::da01af0dff11a32c4dfd6b87db1edf1b
https://pubmed.ncbi.nlm.nih.gov/36508316
https://pubmed.ncbi.nlm.nih.gov/36508316
Autor:
Hyun Woo Lee, Kwang Nam Jin, Sohee Oh, Sung-Yoon Kang, Sang Min Lee, In Beom Jeong, Ji Woong Son, Ju Hyuck Han, Eun Young Heo, Jung Gyu Lee, Young Jae Kim, Eun Young Kim, Young Jun Cho
Publikováno v:
Annals of the American Thoracic Society; May2023, Vol. 20 Issue 5, p660-667, 18p
Autor:
Kiwon Song, Ju Hyuck Han
Publikováno v:
Journal of Knowledge Information Technology and Systems. 14:103-110
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b1c20b95f7c98f7744f19c83e2c7de64
https://doi.org/10.34163/jkits.2019.14.1.011
https://doi.org/10.34163/jkits.2019.14.1.011
Autor:
Sohee Oh, Gi Pyo Lee, Kwang Nam Jin, Hyungjin Kim, Young Jae Kim, Ju Hyuck Han, Eun Young Kim, Yong Suk Kim, Young Jun Cho
Publikováno v:
SSRN Electronic Journal.
Background: Commercially available deep learning algorithms (DLAs) for chest radiography (CXR) have been evaluated with variable clinical settings. This study evaluated a DLA for detecting and localizing referable thoracic abnormalities on CXRs in a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c2f4af0ee112b9d3ef67380a5305a44e
https://doi.org/10.2139/ssrn.3763812
https://doi.org/10.2139/ssrn.3763812