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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
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