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Samuel Hawkins
Samuel Hawkins
Verified email at fsmail.bradley.edu
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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
2662016
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
1642016
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
1332014
Predicting malignant nodules by fusing deep features with classical radiomics features
R Paul, SH Hawkins, MB Schabath, RJ Gillies, LO Hall, DB Goldgof
Journal of Medical Imaging 5 (1), 011021-011021, 2018
882018
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
762016
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
342018
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
302018
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
302018
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
152015
Data from QIN_LUNG_CT
D Goldgof, L Hall, S Hawkins, M Schabath, O Stringfield, A Garcia, ...
Cancer Imaging Arch, 2017
92017
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
82016
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, ...
8
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), 3785, 2021
52021
A Comprehensive Review of Deep Learning-Based Methods for COVID-19 Detection Using Chest X-Ray Images
SS Alahmari, B Altazi, J Hwang, S Hawkins, T Salem
IEEE Access, 2022
32022
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, 805-811, 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
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, ...
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