Automated Detection and Classification in Lung Cancer Screening
Principal Investigator
Name
David Bates
Degrees
MD
Institution
N/A
Position Title
Radiologist
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-233
Initial CDAS Request Approval
Aug 15, 2016
Title
Automated Detection and Classification in Lung Cancer Screening
Summary
As lung cancer screening continues to grow in the US, the burden on radiologists to interpret a large number of screening CT's is growing. Through the use of computer assisted diagnosis and deep learning, we aim to develop software that can detect and classify findings on lung cancer screening CT that may serve as a diagnostic aid to the practicing radiologist.
The system we are developing would work primarily to assist radiologist workflow by automating nodule detection. We are most interested in reviewing as many lung cancer screening CT's with nodules as we can, as this helps the computer learn. We will be using neural networks and deep learning computer models.
The system we are developing would work primarily to assist radiologist workflow by automating nodule detection. We are most interested in reviewing as many lung cancer screening CT's with nodules as we can, as this helps the computer learn. We will be using neural networks and deep learning computer models.
Aims
1. Develop software that can identify pulmonary nodules on lung cancer screening computed tomography with the accuracy that rivals or exceeds that of a practicing radiologist
2. Develop automated techniques to directly track nodules over time to detect subtle changes
Collaborators
Courosh Mehanian
Shawn McGuire