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Using Contrastive Masked Video Autoencoders to detect lung cancer in high-risk individuals with low-dose CT scans

Principal Investigator

Name
Bryan Jiang

Degrees
GED

Institution
STEM ADEMIA LLC

Position Title
Machine Learning Engineer

Email
bryanjiang2004@gmail.com

About this CDAS Project

Study
NLST (Learn more about this study)

Project ID
NLST-1183

Initial CDAS Request Approval
Jan 9, 2024

Title
Using Contrastive Masked Video Autoencoders to detect lung cancer in high-risk individuals with low-dose CT scans

Summary
Interpretation of medical imagery is essential in the triaging and diagnosing cancer. Lung cancer remains the foremost cause of cancer death around the world. Low-dose computed tomographic screening is a tool that can reduce mortality by up to 20%. This project aims to develop a deep learning system that can provide efficient and accurate lung cancer risk scores through computer tomography (CT) images. We will experiment with contrastive masked video autoencoders pre-trained on video data. We believe these new architectures can yield improvements in this space.

Research in the area of contrastive learning and masked auto-encoders has made significant improvements recently. Using a Video Transformers backbone, we hope to combine the advantages of contrastive learning with that of masked autoencoders to produce state-of-the-art classification results.

Aims

1) Develop a reliable AI model which can accurately predict lung cancer risk from low-dose CT
2) Run simulations on lung cancer CT data to test and validate our results
3) Extend the foundation video model to other data

Collaborators

none