Skip to Main Content

An official website of the United States government

Government Funding Lapse

Because of a lapse in government funding, the information on this website may not be up to date, transactions submitted via the website may not be processed, and the agency may not be able to respond to inquiries until appropriations are enacted. The NIH Clinical Center (the research hospital of NIH) is open. For more details about its operating status, please visit  cc.nih.gov. Updates regarding government operating status and resumption of normal operations can be found at OPM.gov.

Principal Investigator
Name
Elliot Smith
Degrees
Ph.D
Institution
Maxwell MRI
Position Title
CTO
Email
About this CDAS Project
Study
NLST (Learn more about this study)
Project ID
NLST-365
Initial CDAS Request Approval
Oct 27, 2017
Title
Deep Learning on Lung CT
Summary
The project will assess the viability of deep learning in the evaluation of low dose lung CT as an automated screening process. This is aimed in particular at regions with access to low dose CT but low access to expert radiologists.
Aims

Determine if a deep learning algorithm can be designed to accurately detect clinically significant lesions in low dose lung CT. This will involve both implementation of existing work as a benchmark and development of new algorithms.

Collaborators

Rebecca Simpson, Maxwell MRI
A/Prof Andrew Bradley, Maxwell MRI
Paula Tattam, Maxwell MRI