Detection of Chronic Obstructive pulmonary Disease from chest CT images using weakly supervised deep learning approach
Principal Investigator
Name
Mengfan Xue
Degrees
Ph.D.
Institution
Hangzhou Dianzi University
Position Title
Associate professor
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-912
Initial CDAS Request Approval
Apr 25, 2022
Title
Detection of Chronic Obstructive pulmonary Disease from chest CT images using weakly supervised deep learning approach
Summary
The accurate diagnosis of chronic obstructive pulmonary disease (COPD) is crucial for the timely initiation of appropriate therapeutic intervention. Previous studies have reported that an estimated of over 40% of COPD patients remain undiagnosed, especially in developing countries. While many pre-defined computed tomographic (CT) measures have been utilized to characterize COPD, it is still challenging to represent pathological alternations of multiple dimensions and highly spatial heterogeneity. we aim to develop an attention-based deep learning model for automated detection of COPD among natural population via CT images.
Aims
We aim to develop an attention-based deep learning model that utilizes CT images for automated detection of COPD among natural population.
Collaborators
Shishen Jia, Hangzhou Dianzi University.