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

Robert Jeraj

Showing results (91-100 of 154) with videos related to

Pageof 16
Sort By:
Medical Physics|March 6, 2004
Radiation characteristics of helical tomotherapyRobert Jeraj, Thomas R Mackie, John Balog, et al.
Physics in Medicine and Biology|November 21, 2018
Automated classification of benign and malignant lesions in <sup>18</sup>F-NaF PET/CT images using machine learningTimothy Perk, Tyler Bradshaw, Song Chen, et al.
Plos One|June 17, 2025
Prediction of future aging-related slow gait and its determinants with deep learning and logistic regressionAlison Deatsch, Michael McKenna, Jonathan Palumbo, et al.
Physics in Medicine and Biology|September 9, 2020
Comparison of 11 automated PET segmentation methods in lymphomaAmy J Weisman, Minnie W Kieler, Scott Perlman, et al.
Physics in Medicine and Biology|March 8, 2024
An automated methodology for whole-body, multimodality tracking of individual cancer lesionsVictor Santoro-Fernandes, Daniel T Huff, Luciano Rivetti, et al.
Radiotherapy and Oncology : Journal of the European Society for Therapeutic Radiology and Oncology|June 12, 2012
Spatially resolved regression analysis of pre-treatment FDG, FLT and Cu-ATSM PET from post-treatment FDG PET: an exploratory studyStephen R Bowen, Richard J Chappell, Søren M Bentzen, et al.
Journal of Medical Imaging (Bellingham, Wash.)|June 29, 2026
Illustration of transfer learning from breast cancer detection to risk prediction: adaptation to local data and local objectivesTobias Wagner, Zan Klanecek, Yao-Kuan Wang, et al.
Journal of Immunotherapy (Hagerstown, Md. : 1997)|July 9, 2025
ctDNA Dynamics Identifies Pseudoprogression in a Metastatic Melanoma Patient Treated With Nivolumab/RelatlimabAlyssa K Steimle, Steve Y Cho, Nandakumar Menon, et al.
Radiology. Artificial Intelligence|May 3, 2021
Convolutional Neural Networks for Automated PET/CT Detection of Diseased Lymph Node Burden in Patients with LymphomaAmy J Weisman, Minnie W Kieler, Scott B Perlman, et al.
La Radiologia Medica|October 30, 2025
Deep learning-based PSMA PET segmentation repeatability: A post-hoc analysis of a single-center, prospective, test-retest trialJake Kendrick, Roslyn J Francis, Ghulam Mubashar Hassan, et al.
Pageof 16

Showing results (91-100 of 154) with videos related to

Sort By:
Pageof 16
Medical Physics|March 6, 2004
Radiation characteristics of helical tomotherapyRobert Jeraj, Thomas R Mackie, John Balog, et al.
Physics in Medicine and Biology|November 21, 2018
Automated classification of benign and malignant lesions in <sup>18</sup>F-NaF PET/CT images using machine learningTimothy Perk, Tyler Bradshaw, Song Chen, et al.
Plos One|June 17, 2025
Prediction of future aging-related slow gait and its determinants with deep learning and logistic regressionAlison Deatsch, Michael McKenna, Jonathan Palumbo, et al.
Physics in Medicine and Biology|September 9, 2020
Comparison of 11 automated PET segmentation methods in lymphomaAmy J Weisman, Minnie W Kieler, Scott Perlman, et al.
Physics in Medicine and Biology|March 8, 2024
An automated methodology for whole-body, multimodality tracking of individual cancer lesionsVictor Santoro-Fernandes, Daniel T Huff, Luciano Rivetti, et al.
Radiotherapy and Oncology : Journal of the European Society for Therapeutic Radiology and Oncology|June 12, 2012
Spatially resolved regression analysis of pre-treatment FDG, FLT and Cu-ATSM PET from post-treatment FDG PET: an exploratory studyStephen R Bowen, Richard J Chappell, Søren M Bentzen, et al.
Journal of Medical Imaging (Bellingham, Wash.)|June 29, 2026
Illustration of transfer learning from breast cancer detection to risk prediction: adaptation to local data and local objectivesTobias Wagner, Zan Klanecek, Yao-Kuan Wang, et al.
Journal of Immunotherapy (Hagerstown, Md. : 1997)|July 9, 2025
ctDNA Dynamics Identifies Pseudoprogression in a Metastatic Melanoma Patient Treated With Nivolumab/RelatlimabAlyssa K Steimle, Steve Y Cho, Nandakumar Menon, et al.
Radiology. Artificial Intelligence|May 3, 2021
Convolutional Neural Networks for Automated PET/CT Detection of Diseased Lymph Node Burden in Patients with LymphomaAmy J Weisman, Minnie W Kieler, Scott B Perlman, et al.
La Radiologia Medica|October 30, 2025
Deep learning-based PSMA PET segmentation repeatability: A post-hoc analysis of a single-center, prospective, test-retest trialJake Kendrick, Roslyn J Francis, Ghulam Mubashar Hassan, et al.
Pageof 16