Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Filters

Zhenyu Shu

Showing results (21-30 of 65) with videos related to

Pageof 7
Sort By:
Plos One|January 27, 2018
Fast and robust shape diameter functionShuangmin Chen, Taijun Liu, Zhenyu Shu, et al.
BMC Neurology|March 13, 2026
Prediction of depression development in Parkinson's disease patients: a radiomics-based machine learning studyYizhou Yuan, Zihan Zhang, Zhenyu Shu, et al.
Frontiers in Aging Neuroscience|January 4, 2021
An Integrative Nomogram for Identifying Early-Stage Parkinson's Disease Using Non-motor Symptoms and White Matter-Based Radiomics Biomarkers From Whole-Brain MRIZhenyu Shu, Peipei Pang, Xiao Wu, et al.
Scientific Reports|May 23, 2024
A combinatorial MRI sequence-based radiomics model for preoperative prediction of microsatellite instability status in rectal cancerXiaowei Xing, Dongxue Li, Jiaxuan Peng, et al.
Journal of Magnetic Resonance Imaging : JMRI|February 9, 2021
The Nomogram of MRI-based Radiomics with Complementary Visual Features by Machine Learning Improves Stratification of Glioblastoma Patients: A Multicenter StudyYuyun Xu, Xiaodong He, Yumei Li, et al.
Zhonghua Wei Chang Wai Ke Za Zhi = Chinese Journal of Gastrointestinal Surgery|October 1, 2018
[Application value of texture analysis of magnetic resonance images in prediction of neoadjuvant chemoradiotherapy efficacy for rectal cancer]Zhenyu Shu, Songhua Fang, Zhongxiang Ding, et al.
IEEE Transactions on Visualization and Computer Graphics|January 11, 2019
Scribble-Based 3D Shape Segmentation via Weakly-Supervised LearningZhenyu Shu, Xiaoyong Shen, Shiqing Xin, et al.
Abdominal Radiology (New York)|March 11, 2019
Prediction of efficacy of neoadjuvant chemoradiotherapy for rectal cancer: the value of texture analysis of magnetic resonance imagesZhenyu Shu, Songhua Fang, Qin Ye, et al.
Journal of X-Ray Science and Technology|September 14, 2020
Application of texture analysis based on T2-weighted magnetic resonance images in discriminating Gleason scores of prostate cancerRuigen Pan, Xueli Yang, Zhenyu Shu, et al.
BMC Cancer|April 21, 2023
Develop and validate a radiomics space-time model to predict the pathological complete response in patients undergoing neoadjuvant treatment of rectal cancer: an artificial intelligence model study based on machine learningJiaxuan Peng, Wei Wang, Hui Jin, et al.
Pageof 7

Showing results (21-30 of 65) with videos related to

Sort By:
Pageof 7
Plos One|January 27, 2018
Fast and robust shape diameter functionShuangmin Chen, Taijun Liu, Zhenyu Shu, et al.
BMC Neurology|March 13, 2026
Prediction of depression development in Parkinson's disease patients: a radiomics-based machine learning studyYizhou Yuan, Zihan Zhang, Zhenyu Shu, et al.
Frontiers in Aging Neuroscience|January 4, 2021
An Integrative Nomogram for Identifying Early-Stage Parkinson's Disease Using Non-motor Symptoms and White Matter-Based Radiomics Biomarkers From Whole-Brain MRIZhenyu Shu, Peipei Pang, Xiao Wu, et al.
Scientific Reports|May 23, 2024
A combinatorial MRI sequence-based radiomics model for preoperative prediction of microsatellite instability status in rectal cancerXiaowei Xing, Dongxue Li, Jiaxuan Peng, et al.
Journal of Magnetic Resonance Imaging : JMRI|February 9, 2021
The Nomogram of MRI-based Radiomics with Complementary Visual Features by Machine Learning Improves Stratification of Glioblastoma Patients: A Multicenter StudyYuyun Xu, Xiaodong He, Yumei Li, et al.
Zhonghua Wei Chang Wai Ke Za Zhi = Chinese Journal of Gastrointestinal Surgery|October 1, 2018
[Application value of texture analysis of magnetic resonance images in prediction of neoadjuvant chemoradiotherapy efficacy for rectal cancer]Zhenyu Shu, Songhua Fang, Zhongxiang Ding, et al.
IEEE Transactions on Visualization and Computer Graphics|January 11, 2019
Scribble-Based 3D Shape Segmentation via Weakly-Supervised LearningZhenyu Shu, Xiaoyong Shen, Shiqing Xin, et al.
Abdominal Radiology (New York)|March 11, 2019
Prediction of efficacy of neoadjuvant chemoradiotherapy for rectal cancer: the value of texture analysis of magnetic resonance imagesZhenyu Shu, Songhua Fang, Qin Ye, et al.
Journal of X-Ray Science and Technology|September 14, 2020
Application of texture analysis based on T2-weighted magnetic resonance images in discriminating Gleason scores of prostate cancerRuigen Pan, Xueli Yang, Zhenyu Shu, et al.
BMC Cancer|April 21, 2023
Develop and validate a radiomics space-time model to predict the pathological complete response in patients undergoing neoadjuvant treatment of rectal cancer: an artificial intelligence model study based on machine learningJiaxuan Peng, Wei Wang, Hui Jin, et al.
Pageof 7