Developing algorithms for the computer-aided detection and diagnosis of lung cancer
Principal Investigator
Name
Jun Zhao
Degrees
Ph.D.
Institution
School of Biomedical Engineering, Shanghai Jiao Tong University
Position Title
Professor
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-557
Initial CDAS Request Approval
Aug 26, 2019
Title
Developing algorithms for the computer-aided detection and diagnosis of lung cancer
Summary
Lung cancer has the highest mortality among all malignancies worldwide. Early diagnosis and treatment can improve the 5-year survival rate of lung cancer. However, it is time-consuming and subjective for manual detection of lung cancer, thus automatic detection algorithms are supposed to be proposed. Nowadays, driven by the development of computer vision and the accessibility of big data, deep learning has achieved fairly good results in medical image processing. In this project, we aim to apply deep learning and transfer learning on lung cancer detection in low-dose computed tomography (LDCT). In addition, once detected, more biological information in LDCT and pathological images of the lung cancer patients will be mined to provide supplementary information for doctor's diagnosis and treatment.
Aims
1.Using deep learning and transfer learning techniques to detect lung cancer in the screening population with LDCT images.
2.For patients with lung cancer, using deep learning methods to mine more biological information, such as the tumor subtypes, degree of tumor invasion, lymph node metastasis, survival risk, etc with LDCT and pathological images.
Collaborators
Weikang Zhang, School of Biomedical Engineering, Shanghai Jiao Tong University
Runping Hou, School of Biomedical Engineering, Shanghai Jiao Tong University
Wangyuan Zhao, School of Biomedical Engineering, Shanghai Jiao Tong University
Xiaowei Xu, School of Biomedical Engineering, Shanghai Jiao Tong University
Lu Zhao, School of Biomedical Engineering, Shanghai Jiao Tong University