Towards Explainable Deep Learning Model for Lung Nodule Diagnosis
Principal Investigator
Name
Chenglong Wang
Degrees
Ph.D.
Institution
East China Normal University
Position Title
Post-doc
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-1141
Initial CDAS Request Approval
Oct 19, 2023
Title
Towards Explainable Deep Learning Model for Lung Nodule Diagnosis
Summary
Lung cancer has the highest mortality rate of all deadly cancers in the world. Early detection is essential to the treatment of lung cancer. However, detection and accurate diagnosis of pulmonary nodules depend heavily on the experience of radiologists and can be a heavy workload for them. Computer-aided diagnosis (CAD) systems have been developed to assist radiologists in nodule detection and diagnosis, greatly easing the workload while increasing diagnosis accuracy. Recent developments in deep learning have greatly improved the performance of CAD systems. However, the lack of model reliability and interpretability remains a major obstacle for its large-scale clinical application. This project aims to develop an explainable deep-learning model for pulmonary nodule diagnosis, which can help radiologists better understand the diagnosis of the AI model.
Aims
- Develop an reliability and interpretability AI model that can provide radiologists with easily understandable explanations
- The developed AI model should achieve high accuracy in classifying data while maintaining an acceptable level of interpretability.
Collaborators
Department of Radiology, the First Affiliated Hospital of Nanjing Medical University