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Automated Feature Learning for Early Diagnosis of Lung Cancer using Low-Dose CT Scans

Principal Investigator
Name
Andrew Berlin
Degrees
Ph.D.
Institution
Draper
Position Title
Distinguished Member of Technical Staff
Email
About this CDAS Project
Study
NLST (Learn more about this study)
Project ID
NLST-417
Initial CDAS Request Approval
Jun 5, 2018
Title
Automated Feature Learning for Early Diagnosis of Lung Cancer using Low-Dose CT Scans
Summary
This project aims to develop deep learning methods to automatically learn informative and discriminatory features for Computer Aided Detection (CADe) of lung nodules and Computer Aided Diagnosis (CADx) of lung cancer.
Aims

- Develop deep learning based CADe models for lung nodule detection.
- Develop deep learning based CADx models for lung cancer diagnosis.
- Analyze and interpret automatically learned features for CADe and CADx.

Collaborators

Onur Ozdemir (Draper)
Rebecca L. Russell (Draper)