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.

Deep learning in lung cancer CAD

Principal Investigator
Name
Xin Zhong
Degrees
Master's degree
Institution
12 Sigma Technologies
Position Title
CEO
Email
About this CDAS Project
Study
NLST (Learn more about this study)
Project ID
NLST-191
Initial CDAS Request Approval
Jan 25, 2016
Title
Deep learning in lung cancer CAD
Summary
We are to design a lung cancer CAD system which applies deep learning techniques in lung cancer nodule detection. Further more, we will apply similar machine learning methods to achieve autonomous lung cancer diagnosis. Our approach is a data driven one that utilizes the spiral lung CT scans which has been labeled by the physicians, and our immediate goal is to achieve detection accuracy higher than all those in all known lung cancer CAD methods.
Aims

High precision sensitivity (>95%) in lung cancer nodule detection with the selectivity > 25% in all types and sizes of lung cancer nodules.

Collaborators

Dashan Gao, 12 Sigma Technologies
Shigao Chen, Mayo Clinic, Mayo Graduate School
Yuxin Li, VA Greater Los Angeles Healthcare System