Samuel Hawkins
Samuel Hawkins
Moffitt Cancer Center
Vahvistettu sähköpostiosoite verkkotunnuksessa mail.usf.edu
Nimike
Viittaukset
Viittaukset
Vuosi
Predicting malignant nodules from screening CT scans
S Hawkins, H Wang, Y Liu, A Garcia, O Stringfield, H Krewer, Q Li, ...
Journal of Thoracic Oncology 11 (12), 2120-2128, 2016
1922016
Deep feature transfer learning in combination with traditional features predicts survival among patients with lung adenocarcinoma
R Paul, SH Hawkins, Y Balagurunathan, M Schabath, RJ Gillies, LO Hall, ...
Tomography 2 (4), 388-395, 2016
1052016
Predicting outcomes of nonsmall cell lung cancer using CT image features
SH Hawkins, JN Korecki, Y Balagurunathan, Y Gu, V Kumar, S Basu, ...
IEEE access 2, 1418-1426, 2014
952014
Predicting malignant nodules by fusing deep features with classical radiomics features
R Paul, S Hawkins, MB Schabath, RJ Gillies, LO Hall, DB Goldgof
Journal of Medical Imaging 5 (1), 011021, 2018
562018
Combining deep neural network and traditional image features to improve survival prediction accuracy for lung cancer patients from diagnostic CT
R Paul, SH Hawkins, LO Hall, DB Goldgof, RJ Gillies
2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC …, 2016
492016
Delta radiomic features improve prediction for lung cancer incidence: A nested case–control analysis of the National Lung Screening Trial
D Cherezov, SH Hawkins, DB Goldgof, LO Hall, Y Liu, Q Li, ...
Cancer medicine 7 (12), 6340-6356, 2018
192018
Investigating multi-radiomic models for enhancing prediction power of cervical cancer treatment outcomes
BA Altazi, DC Fernandez, GG Zhang, S Hawkins, SM Naqvi, Y Kim, ...
Physica Medica 46, 180-188, 2018
192018
Prediction of pathological nodal involvement by CT‐based Radiomic features of the primary tumor in patients with clinically node‐negative peripheral lung adenocarcinomas
Y Liu, J Kim, Y Balagurunathan, S Hawkins, O Stringfield, MB Schabath, ...
Medical physics 45 (6), 2518-2526, 2018
172018
A robust approach for automated lung segmentation in thoracic CT
H Zhou, DB Goldgof, S Hawkins, L Wei, Y Liu, D Creighton, RJ Gillies, ...
2015 IEEE International Conference on Systems, Man, and Cybernetics, 2267-2272, 2015
102015
Improving malignancy prediction through feature selection informed by nodule size ranges in NLST
D Cherezov, S Hawkins, D Goldgof, L Hall, Y Balagurunathan, RJ Gillies, ...
2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC …, 2016
52016
Deep feature transfer learning in combination with traditional features predicts survival among patients with lung adenocarcinoma. Tomography. 2016; 2: 388–95
R Paul, SH Hawkins, Y Balagurunathan, MB Schabath, RJ Gillies, LO Hall
5
Lung CT radiomics: an overview of using images as data
SH Hawkins
University of South Florida, 2017
12017
Change descriptors for determining nodule malignancy in National Lung Screening Trial CT screening images
B Geiger, S Hawkins, LO Hall, DB Goldgof, Y Balagurunathan, ...
Medical Imaging 2016: Computer-Aided Diagnosis 9785, 978535, 2016
12016
Identification of sarcomatoid differentiation in renal cell carcinoma by machine learning on multiparametric MRI
A Mazin, SH Hawkins, O Stringfield, J Dhillon, BJ Manley, DK Jeong, ...
Scientific Reports 11 (1), 1-12, 2021
2021
Predicting malignant nodules from screening CT scans (vol 11, pg 2120, 2016)
S Hawkins, H Wang, Y Liu
JOURNAL OF THORACIC ONCOLOGY 13 (2), 280-281, 2018
2018
P1. 03-063 Quantitative Imaging Features Predict Incidence Lung Cancer in Low-Dose Computed Tomography (LDCT) Screening: Topic: Screening
D Cherezov, S Hawkins, D Goldgof, L Hall, Y Balagurunathan, R Gillies, ...
Journal of Thoracic Oncology 12 (1), S582, 2017
2017
MRI Predictors of Response to Pembrolizumab, Bevacizumab and Hypofractionated Stereotactic Irradiation in Patients with Recurrent High Grade Gliomas
S Hawkins, O Stringfield, N Rognin, J Arrington, M Yu, H Enderling, ...
Järjestelmä ei voi suorittaa toimenpidettä nyt. Yritä myöhemmin uudelleen.
Artikkelit 1–17