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

Peter Q Lee

Showing results (1-10 of 4) with videos related to

Pageof 1
Sort By:
Frontiers in Robotics and AI|June 20, 2022
Optimization-Based Motion Generation for Buzzwire Tasks With the REEM-C Humanoid RobotPeter Q Lee, Vidyasagar Rajendran, Katja Mombaur
Scientific Reports|July 2, 2025
Case study on force compliant robot arm controller for nasopharyngeal swab insertionPeter Q Lee, John S Zelek, Katja Mombaur
Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society|May 23, 2019
Model-free prostate cancer segmentation from dynamic contrast-enhanced MRI with recurrent convolutional networks: A feasibility studyPeter Q Lee, Alessandro Guida, Steve Patterson, et al.
Computer Methods and Programs in Biomedicine|September 9, 2021
Combined Transfer Learning and Test-Time Augmentation Improves Convolutional Neural Network-Based Semantic Segmentation of Prostate Cancer from Multi-Parametric MR ImagesDavid Hoar, Peter Q Lee, Alessandro Guida, et al.
Pageof 1

Showing results (1-10 of 4) with videos related to

Sort By:
Pageof 1
Frontiers in Robotics and AI|June 20, 2022
Optimization-Based Motion Generation for Buzzwire Tasks With the REEM-C Humanoid RobotPeter Q Lee, Vidyasagar Rajendran, Katja Mombaur
Scientific Reports|July 2, 2025
Case study on force compliant robot arm controller for nasopharyngeal swab insertionPeter Q Lee, John S Zelek, Katja Mombaur
Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society|May 23, 2019
Model-free prostate cancer segmentation from dynamic contrast-enhanced MRI with recurrent convolutional networks: A feasibility studyPeter Q Lee, Alessandro Guida, Steve Patterson, et al.
Computer Methods and Programs in Biomedicine|September 9, 2021
Combined Transfer Learning and Test-Time Augmentation Improves Convolutional Neural Network-Based Semantic Segmentation of Prostate Cancer from Multi-Parametric MR ImagesDavid Hoar, Peter Q Lee, Alessandro Guida, et al.
Pageof 1