Samuel H. Hawkins

Change descriptors for determining nodule malignancy in national lung screening trial CT screening images

Robert J. Gillies, Lawrence O. Hall, Dmitry B. Goldgof, 2016, SPIE Medical Imaging.

Predicting Outcomes of Nonsmall Cell Lung Cancer Using CT Image Features

Robert J. Gillies, Lawrence O. Hall, Dmitry B. Goldgof, 2014, IEEE Access.

A Robust Approach for Automated Lung Segmentation in Thoracic CT

Robert J. Gillies, Ying Liu, Saeid Nahavandi, 2015, 2015 IEEE International Conference on Systems, Man, and Cybernetics.

Combining deep neural network and traditional image features to improve survival prediction accuracy for lung cancer patients from diagnostic CT

Robert J. Gillies, Lawrence O. Hall, Dmitry B. Goldgof, 2016, 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

Improving malignancy prediction through feature selection informed by nodule size ranges in NLST

Robert J. Gillies, Lawrence O. Hall, Dmitry B. Goldgof, 2016, 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

Predicting Malignant Nodules from Screening CT Scans

Samuel H. Hawkins, Y. Liu, R. Gillies, 2016, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.

Deep Feature Transfer Learning in Combination with Traditional Features Predicts Survival Among Patients with Lung Adenocarcinoma

Samuel H. Hawkins, R. Gillies, L. Hall, 2016, Tomography.

Reproducibility and Prognosis of Quantitative Features Extracted from CT Images.

Samuel H. Hawkins, Yuhua Gu, Robert J Gillies, 2014, Translational oncology.

Reproducibility and Prognosis of Quantitative Features

Samuel H. Hawkins, R. Gillies, L. Hall, 2014 .

Delta radiomic features improve prediction for lung cancer incidence: A nested case–control analysis of the National Lung Screening Trial

Samuel H. Hawkins, Matthew B Schabath, Dmitry Cherezov, 2018, Cancer medicine.

Investigating multi-radiomic models for enhancing prediction power of cervical cancer treatment outcomes.

Geoffrey G. Zhang, Samuel H. Hawkins, E. Moros, 2018, Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics.

Prediction of pathological nodal involvement by CT‐based Radiomic features of the primary tumor in patients with clinically node‐negative peripheral lung adenocarcinomas

Samuel H. Hawkins, Y. Liu, R. Gillies, 2018, Medical physics.

Predicting malignant nodules by fusing deep features with classical radiomics features

Samuel H. Hawkins, Matthew B Schabath, Rahul Paul, 2018, Journal of medical imaging.

Identification of sarcomatoid differentiation in renal cell carcinoma by machine learning on multiparametric MRI

Samuel H. Hawkins, N. Raghunand, O. Stringfield, 2021, Scientific Reports.