Improvement of the Accuracy in Detection of pulmonary nodules
Principal Investigator
Name
Wei-Ying Chen
Degrees
Ph.D.
Institution
International Integrated Systems, Inc
Position Title
Product Manager
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-631
Initial CDAS Request Approval
Jan 27, 2020
Title
Improvement of the Accuracy in Detection of pulmonary nodules
Summary
A good CAD software tool can help radiologists detect pulmonary nodules quickly. However, many of the current software have high false positive rates and low accuracy in detection of pulmonary nodules, which hinder the efficiency and reduce the willingness to use. We aim to develop and clinically validate software that will help physicians and radiologists identify pulmonary nodules in low-dose chest CT scans. This software will use deep learning to identify pulmonary nodules. Most of the data will be used for training the model, and the remainder will be used for validation.
Aims
- Develop a deep learning model to identify pulmonary nodules.
- Develop a software tool that can detect the pulmonary nodules.
- Validate the tool developed.
Collaborators
Chen Ming,Mao, International Integrated Systems, Inc.
Heng, Chen, International Integrated Systems, Inc.
Yi Lin, Ho, International Integrated Systems, Inc.
Cliff, Wang, International Integrated Systems, Inc.