Zobrazeno 1 - 10
of 11 375
pro vyhledávání: '"Ling, Yu"'
Autor:
Hung, Alex Ling Yu, Zheng, Haoxin, Zhao, Kai, Pang, Kaifeng, Terzopoulos, Demetri, Sung, Kyunghyun
Current deep learning-based models typically analyze medical images in either 2D or 3D albeit disregarding volumetric information or suffering sub-optimal performance due to the anisotropic resolution of MR data. Furthermore, providing an accurate un
Externí odkaz:
http://arxiv.org/abs/2407.01146
Coding, which targets compressing and reconstructing data, and intelligence, often regarded at an abstract computational level as being centered around model learning and prediction, interweave recently to give birth to a series of significant progre
Externí odkaz:
http://arxiv.org/abs/2407.01017
Autor:
Hung, Alex Ling Yu, Zheng, Haoxin, Zhao, Kai, Du, Xiaoxi, Pang, Kaifeng, Miao, Qi, Raman, Steven S., Terzopoulos, Demetri, Sung, Kyunghyun
A large portion of volumetric medical data, especially magnetic resonance imaging (MRI) data, is anisotropic, as the through-plane resolution is typically much lower than the in-plane resolution. Both 3D and purely 2D deep learning-based segmentation
Externí odkaz:
http://arxiv.org/abs/2311.04942
Autor:
Zhang, Donglan, Jin, Lan, Liang, Di, Geng, Ruijin, Liu, Yun, Ling, Yu, Jiang, Fan, Zhang, Yunting
Publikováno v:
JMIR Formative Research, Vol 4, Iss 5, p e17179 (2020)
BackgroundMany children aged younger than 5 years living in low- and middle-income countries are at risk for poor development. Early child development (ECD) programs are cost-effective strategies to reduce poverty, crime, school dropouts, and socioec
Externí odkaz:
https://doaj.org/article/5d6b29d3640a44edb0ba1404e73096ed
Transfer learning has become crucial in computer vision tasks due to the vast availability of pre-trained deep learning models. However, selecting the optimal pre-trained model from a diverse pool for a specific downstream task remains a challenge. E
Externí odkaz:
http://arxiv.org/abs/2308.15074
Denoising diffusion probabilistic models (DDPMs) have achieved impressive performance on various image generation tasks, including image super-resolution. By learning to reverse the process of gradually diffusing the data distribution into Gaussian n
Externí odkaz:
http://arxiv.org/abs/2307.11926
Survival analysis aims at modeling the relationship between covariates and event occurrence with some untracked (censored) samples. In implementation, existing methods model the survival distribution with strong assumptions or in a discrete time spac
Externí odkaz:
http://arxiv.org/abs/2305.14655
Autor:
Wan-Yin Kuo, Chien-Cheng Huang, Chi-An Chen, Chung-Han Ho, Ling‑Yu Tang, Hung-Jung Lin, Shih-Bin Su, Jhi-Joung Wang, Chien-Chin Hsu, Ching-Ping Chang, How-Ran Guo
Publikováno v:
Alzheimer’s Research & Therapy, Vol 16, Iss 1, Pp 1-14 (2024)
Abstract Background Heat-related illness (HRI) is commonly considered an acute condition, and its potential long-term consequences are not well understood. We conducted a population-based cohort study and an animal experiment to evaluate whether HRI
Externí odkaz:
https://doaj.org/article/a3cb9cdf6908468abc73e78a22544985
Autor:
Jingteng Chen, Ling Yu, Tian Gao, Xiangyang Dong, Shiyu Li, Yinchu Liu, Jian Yang, Kezhou Xia, Yaru Yu, Yingshuo Li, Sen Wang, ZhengFu Fan, Hongbing Deng, Weichun Guo
Publikováno v:
Bioactive Materials, Vol 37, Iss , Pp 459-476 (2024)
Magnesium phosphate bone cements (MPC) have been recognized as a viable alternative for bone defect repair due to their high mechanical strength and biodegradability. However, their poor porosity and permeability limit osteogenic cell ingrowth and va
Externí odkaz:
https://doaj.org/article/5081e3b66d26416689e98185038341d3
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-11 (2024)
Abstract This study aims to assess the association between nicotine replacement therapy (NRT), varenicline, and untreated smoking with the risk of developing eye disorders. We employed a new-user design to investigate the association between NRT use
Externí odkaz:
https://doaj.org/article/3b2225de04924caca4f2f7c44ae59ca3