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Principal Investigator
Paul Sajda
Columbia University
Position Title
Professor of Biomedical Engineering
About this CDAS Project
NLST (Learn more about this study)
Project ID
Initial CDAS Request Approval
Aug 23, 2016
Automatic Lung Nodule Detection
Lung cancer screening is accomplished by examining chest CTs for lung nodules. Nodules are evaluated and tracked over time if they are considered to be potentially cancerous. Finding the nodules is done manually by the the radiologist who must scan through large CT images searching for small nodules. Some nodules are inevitably missed.

We seek to design a machine learning application to automatically detect lung nodules. We will utilize convolutional neural networks in combination with a variety of pre-processing approaches.

(1) Determine which preprocessing approaches are most effective for improving the accuracy of lung nodule detection.
(2) Identify the most effective deep learning approach to identify nodules.
(3) Build an optimal machine learner and characterize its accuracy.


Nick Waytowich - Columbia University/U.S Army Research Laboratory
Addison Bohannon - U.S. Army Research Laboratory
Vernon Lawhern - U.S. Army Research Laboratory
Brent Lance - U.S. Army Research Laboratory