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Learning informative features for early diagnosis of lung cancer

Principal Investigator
Name
Yong Fan
Degrees
Ph.D.
Institution
University of Pennsylvania
Position Title
Assistant Professor
Email
About this CDAS Project
Study
NLST (Learn more about this study)
Project ID
NLST-294
Initial CDAS Request Approval
May 18, 2018
Title
Learning informative features for early diagnosis of lung cancer
Summary
The goal of our study is to derive clinically useful radiomic signatures from multimodal imaging data for the early diagnosis of lung cancer. Leveraging advances in machine learning and computer vision, we will learn informative imaging features and build effective prediction models to aid the early diagnosis of lung cancer.
Aims

Aim 1: Develop and validate an automatic lung nodal detection method using deep learning techniques.
Aim 2: Develop and validate a deep learning based lung nodal classification method.
Aim 3. Apply our methods to the NLST data set in order to derive individualized indices for early predicting lung cancer.

Collaborators

Center for Biomedical Image Computing and Analytics, the University of Pennsylvania