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COVID-19 is an emerging, rapidly evolving situation.

What people with cancer should know: https://www.cancer.gov/coronavirus

Get the latest public health information from CDC: https://www.coronavirus.gov

Get the latest research information from NIH: https://covid19.nih.gov/

Principal Investigator
Name
Christophe Zimmer
Degrees
Ph.D.
Institution
Institut Pasteur Paris
Position Title
Research Director
Email
About this CDAS Project
Study
NLST (Learn more about this study)
Project ID
NLST-696
Initial CDAS Request Approval
Jul 21, 2020
Title
DeepCoronaScan
Summary
We aim at improving information extraction from CT-scan images in the COVID-19 setting using deep learning, be it for diagnosis, or to test the possibility that features currently undescribed by radiologist could nevertheless prove predictive of short term clinically actionnable events of interest to intensive care clinicians.
Networks proving efficient enough would
- then be deployed on an open platform to be further tested by other teams.
- be subjected to analysis of the features found to be predictive of a particular event, to try to hint at the underlying pathophysiology or anatomical structures involved
Aims

- test or enhance the classification efficiency of published artificial neural networks classifiying chest CT-scans according to COVID19 diagnosis by increasing the number of available images and sources (as currently published networks are usually trained and tested on extremely limited datasets)
- transfer weights from these re-trained neural networks towards networks predicting a new outcome of interest to clinicans, but for which available data for learning is scarce, such as future need for intubation
- make these networks available on our open platform (https://imjoy.io/#/) to facilitate testing and further training by other teams
- describe which anatomical features could underly the neural network predictions

Collaborators

Christophe ZIMMER
Hoa NGUYEN THI THANH
Benoit LELANDAIS
Wei OUYANG
Ilan OBADIA
Cedric THEPENIER