Deep learning model using multi-modal data for lung cancer survival prediction

Principal Investigator

Name
yingying zhu

Degrees
Ph.D.

Institution
University of Texas, Arlington

Position Title
Assistant Professor

Email
yingying.zhu@uta.edu

About this CDAS Project

Study
NLST (Learn more about this study)

Project ID
NLST-684

Initial CDAS Request Approval
Jul 7, 2020

Title
Deep learning model using multi-modal data for lung cancer survival prediction

Summary
This project will focus on developing Bayesian inference models to combine multiple modal medical data such as CT scan, pathology images, and demographic information (gender, smoking) for early diagnosis of lung cancer and lung cancer survival prediction.

Aims

Aim #1: developing a multi-level patch based instance level feature learning method to study the cancer patterns in the pathology images and use it for lung cancer diagnosis and survival time prediction
Aim #2: study the association of CT scans, demographic information and pathology images and combine multi-modal information for the early diagnosis of lung cancer and lung cancer survival prediction.

Collaborators

None