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

Samuel H Hawkins

Showing results (1-10 of 5) with videos related to

Pageof 1
Sort By:
Journal of Medical Imaging (Bellingham, Wash.)|March 30, 2018
Predicting malignant nodules by fusing deep features with classical radiomics featuresRahul Paul, Samuel H Hawkins, Matthew B Schabath, et al.
Scientific Reports|February 16, 2021
Identification of sarcomatoid differentiation in renal cell carcinoma by machine learning on multiparametric MRIAsim Mazin, Samuel H Hawkins, Olya Stringfield, et al.
Tomography (Ann Arbor, Mich.)|January 10, 2017
Deep Feature Transfer Learning in Combination with Traditional Features Predicts Survival Among Patients with Lung AdenocarcinomaRahul Paul, Samuel H Hawkins, Yoganand Balagurunathan, et al.
Cancer Medicine|December 4, 2018
Delta radiomic features improve prediction for lung cancer incidence: A nested case-control analysis of the National Lung Screening TrialDmitry Cherezov, Samuel H Hawkins, Dmitry B Goldgof, et al.
Research Square|October 4, 2023
Rules-based Volumetric Segmentation of Multiparametric MRI for Response Assessment in Recurrent High-Grade GliomaHarshan Ravi, Samuel H Hawkins, Olya Stringfield, et al.
Pageof 1

Showing results (1-10 of 5) with videos related to

Sort By:
Pageof 1
Journal of Medical Imaging (Bellingham, Wash.)|March 30, 2018
Predicting malignant nodules by fusing deep features with classical radiomics featuresRahul Paul, Samuel H Hawkins, Matthew B Schabath, et al.
Scientific Reports|February 16, 2021
Identification of sarcomatoid differentiation in renal cell carcinoma by machine learning on multiparametric MRIAsim Mazin, Samuel H Hawkins, Olya Stringfield, et al.
Tomography (Ann Arbor, Mich.)|January 10, 2017
Deep Feature Transfer Learning in Combination with Traditional Features Predicts Survival Among Patients with Lung AdenocarcinomaRahul Paul, Samuel H Hawkins, Yoganand Balagurunathan, et al.
Cancer Medicine|December 4, 2018
Delta radiomic features improve prediction for lung cancer incidence: A nested case-control analysis of the National Lung Screening TrialDmitry Cherezov, Samuel H Hawkins, Dmitry B Goldgof, et al.
Research Square|October 4, 2023
Rules-based Volumetric Segmentation of Multiparametric MRI for Response Assessment in Recurrent High-Grade GliomaHarshan Ravi, Samuel H Hawkins, Olya Stringfield, et al.
Pageof 1