Zobrazeno 1 - 10
of 171
pro vyhledávání: '"Weili Lin"'
Autor:
Daniel J. Bauer, Margaret A. Sheridan, Douglas H. Clements, Denis Dumas, Julie Sarama, Jessica R. Cohen, Weili Lin, Daniel McNeish
Publikováno v:
Psychol Methods
Individual differences in the timing of developmental processes are often of interest in longitudinal studies, yet common statistical approaches to modeling change cannot directly estimate the timing of when change occurs. The time-to-criterion frame
Autor:
Li Wang, Gang Li, Zhengwang Wu, Fenqiang Zhao, Fan Wang, Weili Lin, Dinggang Shen, Shunren Xia
Publikováno v:
IEEE Trans Med Imaging
Cortical surface registration is an essential step and prerequisite for surface-based neuroimaging analysis. It aligns cortical surfaces across individuals and time points to establish cross-sectional and longitudinal cortical correspondences to faci
Publikováno v:
Med Image Comput Comput Assist Interv
Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 ISBN: 9783030871987
MICCAI (3)
Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 ISBN: 9783030871987
MICCAI (3)
The difficulty of acquiring resting-state fMRI of early developing children under the same condition leads to a dedicated protocol, i.e., scanning younger infants during sleep and older children during being awake, respectively. However, the obviousl
Autor:
Li Zhao, Toan Duc Bui, Qi Dou, Yu Zhang, Sijie Niu, Trung Le Phan, Guannan Li, Longchuan Li, Sarah Shultz, Xiaopeng Zong, Wenao Ma, Gang Li, Yue Sun, Ying Wei, Xue Feng, Mallappa Kumara Swamy, Camilo Bermudez Noguera, Tao Zhong, Valerie Jewells, Li Wang, Weili Lin, Ramesh Basnet, Caizi Li, M. Omair Ahmad, Dinggang Shen, Zhihao Lei, Ian H. Gotlib, Kathryn L. Humphreys, Jun Ma, Bennett A. Landman, Jitae Shin, Kun Gao, Zhengwang Wu, Lequan Yu, Xiaoping Yang
Publikováno v:
IEEE Trans Med Imaging
To better understand early brain development in health and disorder, it is critical to accurately segment infant brain magnetic resonance (MR) images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). Deep learning-based methods
Publikováno v:
Mach Learn Med Imaging
Machine Learning in Medical Imaging ISBN: 9783030875886
MLMI@MICCAI
Machine Learning in Medical Imaging ISBN: 9783030875886
MLMI@MICCAI
Accurate tissue segmentation of large-scale pediatric brain MR images from multiple sites is essential to characterize early brain development. Due to imaging motion/Gibbs artifacts and multi-site issue (or domain shift issue), it remains a challenge
Publikováno v:
IEEE transactions on medical imaging
Fast and automated image quality assessment (IQA) for diffusion MR images is a crucial step for swiftly making a rescan decision during or after the scanning session. However, learning a model for this task is challenging as the number of annotated d
Publikováno v:
Hybrid PET/MR Neuroimaging ISBN: 9783030823665
Hybrid PET/MR Neuroimaging
Hybrid PET/MR Neuroimaging
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::55f9d6f900b272a216685fbd7225d64c
https://doi.org/10.1007/978-3-030-82367-2_9
https://doi.org/10.1007/978-3-030-82367-2_9
Infancy is a dynamic and immensely important period in human brain development. Studies of infant functional development using resting-state fMRI rely on precisely defined cortical parcellation maps. However, available adult-based functional parcella
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c754c9f3c44ad553a6c56863139a0261
https://doi.org/10.1101/2021.11.24.469844
https://doi.org/10.1101/2021.11.24.469844
Publikováno v:
Cereb Cortex
Uncovering the moment-to-moment dynamics of functional connectivity (FC) in the human brain during early development is crucial for understanding emerging complex cognitive functions and behaviors. To this end, this paper leveraged a longitudinal res
Publikováno v:
IEEE Trans Med Imaging
Missing data is a common problem in longitudinal studies due to subject dropouts and failed scans. We present a graph-based convolutional neural network to predict missing diffusion MRI data. In particular, we consider the relationships between sampl