| 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 | 156 | 2016 |
| 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 | 81 | 2014 |
| 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 | 76 | 2016 |
| 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 | 46 | 2018 |
| 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 | 40 | 2016 |
| 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 | 15 | 2018 |
| 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 | 13 | 2018 |
| 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 | 11 | 2018 |
| 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 | 9 | 2015 |
| 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 | 5 | 2016 |
| 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 | 2 | 2019 |
| Lung CT radiomics: an overview of using images as data SH Hawkins | 1 | 2017 |
| 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 | 1 | 2016 |
| 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 |
Robert GilliesChair of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer CenterVerified email at moffitt.org
