Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Guankai Peng"'
Autor:
Yueye Wang, Xiaotong Han, Cong Li, Lixia Luo, Qiuxia Yin, Jian Zhang, Guankai Peng, Danli Shi, Mingguang He
Publikováno v:
Journal of Medical Internet Research, Vol 26, p e52506 (2024)
BackgroundFor medical artificial intelligence (AI) training and validation, human expert labels are considered the gold standard that represents the correct answers or desired outputs for a given data set. These labels serve as a reference or benchma
Externí odkaz:
https://doaj.org/article/369c0afcde064ce8982bb2e1e63a2b66
Autor:
Yueye Wang, Danli Shi, Zachary Tan, Yong Niu, Yu Jiang, Ruilin Xiong, Guankai Peng, Mingguang He
Publikováno v:
Frontiers in Medicine, Vol 8 (2021)
Purpose: To assess the accuracy and efficacy of a semi-automated deep learning algorithm (DLA) assisted approach to detect vision-threatening diabetic retinopathy (DR).Methods: We developed a two-step semi-automated DLA-assisted approach to grade fun
Externí odkaz:
https://doaj.org/article/e5415a0bd07849f2b6c9bdee5cbfde62
Publikováno v:
PLoS ONE, Vol 14, Iss 9, p e0222025 (2019)
PurposeTo provide a self-adaptive deep learning (DL) method to automatically detect the eye laterality based on fundus images.MethodsA total of 18394 fundus images with real-world eye laterality labels were used for model development and internal val
Externí odkaz:
https://doaj.org/article/e96421ba7896467e9563f5a31a92db65
Autor:
Meng Xuan, Wei Wang, Danli Shi, James Tong, Zhuoting Zhu, Yu Jiang, Zongyuan Ge, Jian Zhang, Gabriella Bulloch, Guankai Peng, Wei Meng, Cong Li, Ruilin Xiong, Yixiong Yuan, Mingguang He
Publikováno v:
Translational Vision Science & Technology. 12:22
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::198275b6f861cc1567a9d73dbbcaa5c4
https://doi.org/10.1167/tvst.12.3.22
https://doi.org/10.1167/tvst.12.3.22
Akademický článek
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Autor:
Yu Huang, Honghua Yu, Guankai Peng, Wei Meng, Zhuoting Zhu, Wei Wang, Xueli Zhang, Huan Liao, Xiaohong Yang, Danli Shi, Zachary Tan, Wenyi Hu, Mingguang He, Xianwen Shang
SummaryBackgroundAgeing varies substantially, thus an accurate quantification of ageing is important. We developed a deep learning (DL) model that predicted age from fundus images (retinal age). We investigated the association between retinal age gap
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0e1a0e90ff44515c693a7d1b18d32bd7
https://doi.org/10.1101/2020.12.24.20248817
https://doi.org/10.1101/2020.12.24.20248817
Autor:
Zhu, Zhuoting, Shi, Danli, Guankai, Peng, Tan, Zachary, Shang, Xianwen, Hu, Wenyi, Liao, Huan, Zhang, Xueli, Huang, Yu, Yu, Honghua, Meng, Wei, Wang, Wei, Ge, Zongyuan, Yang, Xiaohong, He, Mingguang
Publikováno v:
British Journal of Ophthalmology; 2023, Vol. 107 Issue: 4 p547-554, 8p
Publikováno v:
PLoS ONE
PLoS ONE, Vol 14, Iss 9, p e0222025 (2019)
PLoS ONE, Vol 14, Iss 9, p e0222025 (2019)
PurposeTo provide a self-adaptive deep learning (DL) method to automatically detect the eye laterality based on fundus images.MethodsA total of 18394 fundus images with real-world eye laterality labels were used for model development and internal val
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e8e34c453dd77ab8dd3f8ed565461d77
https://doi.org/10.1371/journal.pone.0222025
https://doi.org/10.1371/journal.pone.0222025