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.

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.