Canadian startup DarwinAI and researchers from the University of Waterloo are open-sourcing COVID-Net, a convolutional neural network that aims to detect COVID-19 in X-ray imagery. In response to the pandemic, a global community of health care and AI researchers have produced a number of AI systems for identifying COVID-19 in CT scans.
Companies like Alibaba and AI startups RadLogics and Lunit claim they’ve created systems capable of recognizing COVID-19 in X-ray or CT scans with more than 90% accuracy. Early work from Chinese medical researchers and a system published in the journal Radiology last week demonstrated similar results.
“[Though it is by] no means a production-ready solution, the hope is that the open access COVID-Net, along with the description on constructing the open source COVIDx data set, will be leveraged and built upon by both researchers and citizen data scientists alike to accelerate the development of highly accurate yet practical deep learning solutions for detecting COVID-19 cases and accelerate treatment of those who need it the most,” the paper reads.
Dr. Alexander Wong is an associate professor at the Waterloo AI Institute, codirector of the Vision and Image Processing Group at the University of Waterloo, and lead researcher at DarwinAI.
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