| Improvement of Early Lung Cancer Detection Rate for Patients Undergoing 18F-FDG PET/CT scans |
Tsung-Ying Ho |
Chang Gung Memorial Hospital |
NLST |
Feb 9, 2017 |
NLST-284 |
| Surgical approach and outcomes in NLST patients with positive screening CT |
Brendon Stiles |
Weill Cornell Medicine |
NLST |
Feb 7, 2017 |
NLST-281 |
| Medical applications of example-based super-resolution |
Ramin Zabih |
Joan & Sanford I. Weill Medical College of Cornell University |
NLST |
Jan 31, 2017 |
NLST-279 |
| Deep learning of lung cancer images for segmentation and outcome prediction |
Olivier Gevaert |
Stanford University |
NLST |
Jan 31, 2017 |
NLST-276 |
| Lung lesion screening and tracking |
Carla Leibowitz |
Arterys |
NLST |
Jan 30, 2017 |
NLST-278 |
| Lung Cancer Detection using Data Analytics and Machine Learning |
Apoorva Mahale |
Vivekanand Education Society's Institute of Technology |
NLST |
Jan 30, 2017 |
NLST-277 |
| Lung cancer prediction by deep learning approach |
Xiaokang Wang |
University of California,Davis |
NLST |
Jan 26, 2017 |
NLST-273 |
| Continued deep learning modeling for early detection |
Jeremy Howard |
University of San Francisco |
NLST |
Jan 25, 2017 |
NLST-274 |
| SPORE Grant Pipeline |
Paul Kinahan |
University of Washington |
NLST |
Jan 5, 2017 |
NLST-271 |
| Intelligent Lung Cancer Diagnostic Aid System: a RADIOMICS approach applied to histology |
Lucas Lima |
Federal University of Alagoas |
NLST |
Dec 27, 2016 |
NLST-268 |
| Risk modelling of time to lung cancer diagnosis |
Anton Schreuder |
Radboudumc |
NLST |
Dec 27, 2016 |
NLST-267 |
| Medical data mining on small datasets |
Ron Wolfslast |
University Hamburg |
NLST |
Dec 16, 2016 |
NLST-266 |
| MRI screening for lung cancer: Markov modeling using the NLST CT outcomes data |
Mark Schiebler |
UW-Madison School of Medicine and Public Health |
NLST |
Dec 15, 2016 |
NLST-265 |
| Instance-driven semantic segmentation and clinical information assessment of temporal changes for lung nodules |
Kang Li |
Department of Industrial and Systems Engineering |
NLST |
Dec 5, 2016 |
NLST-261 |
| RETROSPECTIVE ANALYSIS OF PULMONARY NODULES USING A NEW ALGORITHM FOR DETERMINING INTERVAL GROWTH, WITH SUBSEQUENT PROSPECTIVE ANALYSIS FOR BLINDED VALIDATION |
John Billings |
Independent Researcher |
NLST |
Dec 5, 2016 |
NLST-263 |
| Automatic lung nodule detection by CNN |
Shuang Wu |
Yitu USA |
NLST |
Dec 2, 2016 |
NLST-258 |
| Early Detection and Prognosis of Lung Cancer |
Yun Li |
UNC Chapel Hill |
NLST |
Dec 2, 2016 |
NLST-262 |
| Statistical models to predict future subject’s lung cancer risk: application to NLST and PLCO data |
Ping Hu |
NCI |
NLST |
Nov 21, 2016 |
NLST-259 |
| Clinical Decision Support System (CDSS) and self-learning tool for radiologists for Lung CT using Content Based Image Retrieval (CBIR). |
Sudipta Mukhopadhyay |
Indian Institute of Technology Kharagpur, India |
NLST |
Nov 10, 2016 |
NLST-255 |
| Longitudinal Deep Radiomic Signatures for Lung Cancer Prognosis and Treatment Response Prediction |
Nicolas Chapados |
Imagia Inc. |
NLST |
Nov 10, 2016 |
NLST-256 |
