Samuel Hawkins
Samuel Hawkins
Moffitt Cancer Center
Verified email at mail.usf.edu
Title
Cited by
Cited by
Year
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
1562016
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
812014
Deep feature transfer learning in combination with traditional features predicts survival among patients with lung adenocarcinoma
R Paul, SH Hawkins, Y Balagurunathan, MB Schabath, RJ Gillies, LO Hall, ...
Tomography 2 (4), 388, 2016
762016
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
462018
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
402016
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
152018
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
132018
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
112018
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
92015
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
Behind the Mask: Understanding the Structural Forces That Make Social Graphs Vulnerable to Deanonymization.
S Horawalavithana, JA Flores, J Skvoretz, A Iamnitchi, T Wang, J Weng, ...
IEEE Trans. Comput. Soc. Syst. 6 (6), 1343-1356, 2019
22019
Lung CT radiomics: an overview of using images as data
SH Hawkins
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
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
The system can't perform the operation now. Try again later.
Articles 1–15