Automatic lung nodule detection by CNN

Principal Investigator

Name
Shuang Wu

Degrees
Ph.D.

Institution
Yitu USA

Position Title
Research Scientist

Email
shuang.wu@gmail.com

About this CDAS Project

Study
NLST (Learn more about this study)

Project ID
NLST-258

Initial CDAS Request Approval
Dec 2, 2016

Title
Automatic lung nodule detection by CNN

Summary
We'd like to apply deep convolutional neural networks to identifying medical conditions based on CT scans.

The first goal is to automatically detect lung nodules.

The second goal is diagnosis of several other types of lung/heart conditions such as Atelectasis, Cardiomegaly, Effusion, Pneumonia, etc.

We have several years' of research experience in computer vision, especially using deep learning approach. The main technique is convolutional neural networks(CNN). We have already developed a prototype, training on a few hundred CT scans, and got some promising results. We feel with more data, we can push accuracy even higher. For example, for nodule detection, we are aiming for sensitivity > 90% when false positive rate is 0.1 nodule/scan. For the lung/heart condition diagnosis task, we are aiming for accuracy > 95%.

Aims

We'd like to increase the lung nodule detection rate to 90% with false positive rate under 1%

Collaborators

Shu Rong, Yitu Inc.