S
Samuel H. Hawkins
Researcher at University of South Florida
Publications - 13
Citations - 697
Samuel H. Hawkins is an academic researcher from University of South Florida. The author has contributed to research in topics: Lung cancer & National Lung Screening Trial. The author has an hindex of 8, co-authored 13 publications receiving 516 citations. Previous affiliations of Samuel H. Hawkins include Bradley University.
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Journal ArticleDOI
Predicting Malignant Nodules from Screening CT Scans
Samuel H. Hawkins,Hua Wang,Ying Liu,Alberto Garcia,Olya Stringfield,Henry Krewer,Qian Li,Dmitry Cherezov,Robert A. Gatenby,Yoganand Balagurunathan,Dmitry B. Goldgof,Matthew B. Schabath,Lawrence O. Hall,Robert J. Gillies +13 more
TL;DR: The radiomics of lung cancer screening computed tomography scans at baseline can be used to assess risk for development of cancer.
Journal ArticleDOI
Deep Feature Transfer Learning in Combination with Traditional Features Predicts Survival Among Patients with Lung Adenocarcinoma.
Rahul Paul,Samuel H. Hawkins,Yoganand Balagurunathan,Matthew B. Schabath,Robert J. Gillies,Lawrence O. Hall,Dmitry B. Goldgof +6 more
TL;DR: This work applied a pretrained CNN to extract deep features from 40 computed tomography images, with contrast, of non-small cell adenocarcinoma lung cancer, and combined deep features with traditional image features and trained classifiers to predict short- and long-term survivors.
Journal ArticleDOI
Predicting Outcomes of Nonsmall Cell Lung Cancer Using CT Image Features
Samuel H. Hawkins,John N. Korecki,Yoganand Balagurunathan,Yuhua Gu,Virendra Kumar,Satrajit Basu,Lawrence O. Hall,Dmitry B. Goldgof,Robert A. Gatenby,Robert J. Gillies +9 more
TL;DR: Focusing on cases of the adenocarcinoma nonsmall cell lung cancer tumor subtype from a larger data set, it is shown that classifiers can be built to predict survival time, the first known result to make such predictions from CT scans of lung cancer.
Journal ArticleDOI
Predicting malignant nodules by fusing deep features with classical radiomics features.
Rahul Paul,Samuel H. Hawkins,Matthew B. Schabath,Robert J. Gillies,Lawrence O. Hall,Dmitry B. Goldgof +5 more
TL;DR: Using subsets of participants from the National Lung Screening Trial (NLST), a transfer learning approach was utilized to differentiate lung cancer nodules versus positive controls and the best accuracy (76.79%) was obtained using feature combinations.
Proceedings ArticleDOI
Combining deep neural network and traditional image features to improve survival prediction accuracy for lung cancer patients from diagnostic CT
TL;DR: This study applies a pre-trained convolutional neural network (CNN) to extract deep features from lung cancer CT images and then train classifiers to predict short and long term survivors.