Zobrazeno 1 - 10
of 494
pro vyhledávání: '"Fung, Kenneth"'
Autor:
Wong, Josephine Pui-Hing, Jia, Cun-Xian, Vahabi, Mandana, Liu, Jenny Jing Wen, Li, Alan Tai-Wai, Cong, Xiaofeng, Poon, Maurice Kwong-Lai, Yamada, Janet, Ning, Xuan, Gao, Jianguo, Cheng, Shengli, Sun, Guoxiao, Wang, Xinting, Fung, Kenneth Po-Lun
Publikováno v:
JMIR Research Protocols, Vol 10, Iss 5, p e25009 (2021)
BackgroundChinese students are extremely vulnerable to developing mental illness. The stigma associated with mental illness presents a barrier to seeking help for their mental health. ObjectiveThe Linking Hearts—Linking Youth and ‘Xin’ (hearts
Externí odkaz:
https://doaj.org/article/9896519b35494a0083cf13a70c026e17
Autor:
SHI, Yin Ru1 (AUTHOR) Shiyinru921020@163.com, SIN, Kuen Fung Kenneth2 (AUTHOR)
Publikováno v:
PLoS ONE. 9/30/2024, Vol. 19 Issue 9, p1-19. 19p.
Autor:
Lake, Johanna, Po-Lun Fung, Kenneth, Steel, Lee, Magnacca, Carly, Cardiff, Katie, Thomson, Kendra, Bobbette, Nicole, Redquest, Brianne, Bailey, Sacha, Lunsky, Yona
Publikováno v:
In Journal of Contextual Behavioral Science July 2024 33
Autor:
Ferdian, Edward, Suinesiaputra, Avan, Fung, Kenneth, Aung, Nay, Lukaschuk, Elena, Barutcu, Ahmet, Maclean, Edd, Paiva, Jose, Piechnik, Stefan K., Neubauer, Stefan, Petersen, Steffen E, Young, Alistair A.
Publikováno v:
Radiology: Cardiothoracic Imaging 2020; 2(1):e190032
Purpose: To demonstrate the feasibility and performance of a fully automated deep learning framework to estimate myocardial strain from short-axis cardiac magnetic resonance tagged images. Methods and Materials: In this retrospective cross-sectional
Externí odkaz:
http://arxiv.org/abs/2004.07064
Akademický článek
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Autor:
Yao, Zhi-ying, Wang, Tao, Yu, Yao-kun, Li, Ran, Sang, Xiao, Fu, Yi-na, Gong, Xiao-jie, Sun, Wen-jun, Liu, Jenny Jing-wen, Wong, Josephine Pui-Hing, Fung, Kenneth Po-Lun, Jia, Cun-xian
Publikováno v:
In Journal of Affective Disorders 15 October 2023 339:293-301
Autor:
Chen, Chen, Bai, Wenjia, Davies, Rhodri H., Bhuva, Anish N., Manisty, Charlotte, Moon, James C., Aung, Nay, Lee, Aaron M., Sanghvi, Mihir M., Fung, Kenneth, Paiva, Jose Miguel, Petersen, Steffen E., Lukaschuk, Elena, Piechnik, Stefan K., Neubauer, Stefan, Rueckert, Daniel
Convolutional neural network (CNN) based segmentation methods provide an efficient and automated way for clinicians to assess the structure and function of the heart in cardiac MR images. While CNNs can generally perform the segmentation tasks with h
Externí odkaz:
http://arxiv.org/abs/1907.01268
We perform unsupervised analysis of image-derived shape and motion features extracted from 3822 cardiac 4D MRIs of the UK Biobank. First, with a feature extraction method previously published based on deep learning models, we extract from each case 9
Externí odkaz:
http://arxiv.org/abs/1902.05811
Autor:
Robinson, Robert, Valindria, Vanya V., Bai, Wenjia, Oktay, Ozan, Kainz, Bernhard, Suzuki, Hideaki, Sanghvi, Mihir M., Aung, Nay, Paiva, Jos$é$ Miguel, Zemrak, Filip, Fung, Kenneth, Lukaschuk, Elena, Lee, Aaron M., Carapella, Valentina, Kim, Young Jin, Piechnik, Stefan K., Neubauer, Stefan, Petersen, Steffen E., Page, Chris, Matthews, Paul M., Rueckert, Daniel, Glocker, Ben
Background: The trend towards large-scale studies including population imaging poses new challenges in terms of quality control (QC). This is a particular issue when automatic processing tools, e.g. image segmentation methods, are employed to derive
Externí odkaz:
http://arxiv.org/abs/1901.09351
Autor:
Etowa, Egbe B.1,2 eetowa@torontomu.ca, Fung, Kenneth3, Miller, Desmond2, Husbands, Winston4, Luginaah, Isaac5, Omorodion, Francisca6, Etowa, Josephine7, Wong, Josephine P.2
Publikováno v:
Canadian Journal of Human Sexuality. Dec2023, Vol. 32 Issue 3, p298-312. 15p.