COVID-Net: More data, CT Scans and Risk Stratification
Dear Colleague,
The response to our release of COVID-Net continues to move and inspire us.
Here are the latest updates with the project:
1.) Thanks to support of the research community, the number of COVID-19 positive cases in the training and test portions of the COVIDx dataset has increased by 2x and 3x, respectively. Instructions and scripts on generating this latest collection can are available on our GitHub repo. We’d especially like to thank Figure 1 for their assistance.
2.) We’ve released two models based on the dataset above: COVIDNet-CXR-Large and COVIDNet-CXR-Small. The former provides higher detection sensitivity for COVID-19 detection, whereas the latter achieves a higher COVID-19 positive-predictive-value. Both are available at the GitHub repo.
Again, our intention is to provide these models as reference models to build upon and improve as new data becomes available and to drive model improvements and innovations. They are not, at present, production-ready models, and we intend on improving them over time and making improvements publicly accessible.
3.) Our team is currently working on COVIDNet-CT, a neural network tailored for COVID-19 detection via CT scans (we are likewise working on augmenting the COVIDx dataset with CT instances). For context, CTs provide superior levels of detail and clarity when compared to conventional X-Rays, and several studies suggest higher levels of sensitivity than viral testing.
With COVIDNet-CT, we hope to give clinicians another tool in screening COVID-19. Per the figure below, the initial results are promising and we hope to make the model available soon.
4.) Finally, our team is hard at work on COVIDNet-Risk, a neural network tailored for COVID-19 risk stratification. Stay tuned as we’ll make it available shortly.
Finally, if you’re a researcher or clinician and would like access to our explainability platform to assist with this project and gain transparency or have data to share, please email us at info@darwinai.ca.
In Solidarity,
The DarwinAI team
