| Deep Learning to Detect Lung Cancer and Predict Mortality |
George Washko |
Brigham and Women's Hospital |
NLST |
Jun 14, 2017 |
NLST-313 |
| Unsupervised feature extraction for benign and malignant pulmonary nodules |
Matthew Stephens |
University of Cincinnati |
NLST |
Jun 13, 2017 |
NLST-314 |
| Lung Cancer Risk in Patients with Interstitial Lung Disease |
Stacey-Ann Whittaker Brown |
Icahn School of Medicine at Mount Sinai |
NLST |
Jun 9, 2017 |
NLST-310 |
| Bayesian probabilistic techniques for cancer growth prediction/ prognosis based on informative features containing significant medical details within the historical CT scans |
Haroon Rasheed |
Bahria University,Karachi - 75620 Pakistan |
NLST |
Jun 8, 2017 |
NLST-307 |
| Predicting medical outcomes using deep learning with CT chest images |
Lyle Palmer |
University of Adelaide |
NLST |
Jun 8, 2017 |
NLST-311 |
| Evaluating comorbidities and life expectancy in patients undergoing LDCT screening in the real world setting |
Jonathan Iaccarino |
Boston University Medical Campus |
NLST |
May 19, 2017 |
NLST-309 |
| Computer Vision AI to Diagnose Lung Cancer from CT Images |
Peter Szoldan |
MedInnoScan Kft. |
NLST |
May 12, 2017 |
NLST-306 |
| Machine Learning Methods for Nodule Detection and Classification |
Xiaohui Xie |
University of California, Irvine |
NLST |
May 11, 2017 |
NLST-304 |
| Outcomes associated with significant incidental findings in lung cancer screening |
Ilana Gareen |
Brown University |
NLST |
May 10, 2017 |
NLST-308 |
| Automatic Detection and Classification System for Lung Lesion |
Takashi Shirahata |
Hitachi, Ltd. |
NLST |
Apr 27, 2017 |
NLST-302 |
| Using Deep Learning for Cancer Nodule Detection |
Ashish Gupta |
Auburn University |
NLST |
Apr 25, 2017 |
NLST-300 |
| Clinical and cost effectiveness of lung cancer screening by low-dose CT |
Chris Hyde |
University of Exeter |
NLST |
Apr 20, 2017 |
NLST-301 |
| Use of neural networks in tissue abnormality detection |
Rafal Grzeszczuk |
AGH University of Science and Technology |
NLST |
Apr 11, 2017 |
NLST-299 |
| Application of Deep Learning to Combine Clinical and Imaging Data to Localize, Characterize, and Prognosticate Lung Cancer Patients |
Jae Ho Sohn |
UCSF |
NLST |
Apr 4, 2017 |
NLST-297 |
| Determining the prevalence and misclassification of perifissural nodules in the NLST. |
Anton Schreuder |
Radboudumc |
NLST |
Mar 22, 2017 |
NLST-296 |
| Detection and Diagnosis of Lung Cancer with Deep Learning |
Shan Li |
Zephex Technology |
NLST |
Mar 21, 2017 |
NLST-295 |
| Automated Lung Nodule Detection using Deep Neural Networks |
Kun-Hsing Yu |
President and Fellows of Harvard College |
NLST |
Feb 22, 2017 |
NLST-286 |
| Building a Common Data Model for Cancer Research |
Guoqian Jiang |
Mayo Clinic |
NLST |
Feb 17, 2017 |
NLST-285 |
| Pulmonary Nodule Detection and Classification using large scale CT data |
feifei zhou |
Independent Researcher, not applicable |
NLST |
Feb 13, 2017 |
NLST-283 |
| Radiomics using Deep Learning with High Performance Computing |
Eduardo Ulises Moya Sánchez |
Barcelona Supercomputing Center |
NLST |
Feb 9, 2017 |
NLST-275 |
