Idea Stage
Actively Recruiting

COVID-Net: an Open Source Initiative for COVID-19 Detection and Risk Stratification using Chest X-rays

Global
Created over 1 year ago
18 Volunteers

About the Project

Description

With the unprecedented number of COVID-19 infections on a global scale, chest X-rays has now become an important screening tool alongside viral testing in countries that are most affected by COVID-19, with key advantages being: i) speed, ii) widespread availability (including portable X-ray systems), ii) provides useful information for risk assessment, which one cannot obtain from viral testing.

The goal of the COVID-Net initiative is to accelerate the open collaborative development of deep learning AI solutions for COVID-19 infection detection and risk stratification, with AI explanations to gain transparency into visual indicators of COVID-19. We have provided not only COVIDx, a large dataset of over 16,000 images across over 13,000 patient cases, but also open source reference models trained on this dataset so that the global community can built upon and improve.

By leveraging deep learning AI with COVID-Net, we aim to help clinicians improve both sensitivity and specificity by better differentiate COVID-19 infections from other forms of viral infections, which is a current challenge faced given their similarities, as well as assist clinicians with additional knowledge about how COVID-Net detects COVID-19 infections through AI explanations. Furthermore, we aim to build deep learning AI for risk stratification to aid hospitals and clinical sites to help improve patient population management and individualized care based on risk level.

How far along is it

Live and available at: https://github.com/lindawangg/COVID-Net
Tremendous progress has been made, with the recent launch of:
- a brand-new, expanded dataset of over 16,000 images across over 13,000 patient cases
- two brand-new open source deep learning models for public use to build upon
- Traction and support from the global community, ready to contribute resources and data.

Help Needed

Tasks that need to get done

We are looking for volunteers to:
- Leverage the dataset to build and investigate state-of-the-art AI models
- Devise new model architectures to improve peformance
- Supply data from different areas of the world so we can build better models as a collective.

Project details

Who is already working on this

COVID-Net was launched by a team of AI scientists at the Vision and Image Processing research group at the University of Waterloo, led by Dr. Alexander Wong, Canada Research Chair in AI and Medical Imaging, and DarwinAI Corp., an award-winning AI startup in Waterloo, ON, Canada.

How to get in touch
Contact me at alex@darwinai.ca or contribute and communicate directly at https://github.com/lindawangg/COVID-Net
Number of volunteers needed
10-50
Preferred Volunteer location
remote
Organization status
Not specified