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Diagnosing COVID-19 from deep learning trained on CT scans

Principal Investigator
Name
Jean-Remi King
Degrees
Ph.D.
Institution
CNRS
Position Title
Tenured Researcher (CRCN)
Email
About this CDAS Project
Study
NLST (Learn more about this study)
Project ID
NLST-650
Initial CDAS Request Approval
Mar 17, 2020
Title
Diagnosing COVID-19 from deep learning trained on CT scans
Summary
Diagnosing COVID19 infection from other mild respiratory diseases is a major priority to limit the current pandemic. Yet, the number of kit tests availble is dramatically low, and MDs are currently relying on CT scans as a substitute. The present projects aims to build a deep learning architecture trained to model CT scans. This deep learning architecture will then transform the incoming CT scans from infected and non-infected patients currently acquired in the clinics, in order to facilitate their discriminability.
Aims

- develop open source preprocessing pipeline of CT scans
- implement U-net deep learning architecture to model NLST data
- provide user-friendly interface to project new CT scan onto the model, and allow supervised or unsupervised analysis

Collaborators

Nathan Pfeiffer Smadja