Skip to the content.

Cancer-Net TB-Net TB-Net

COVID-Net Open Initiative (Cancer-Net, TB-Net, Fibrosis-Net Initiatives)

Launched in March 2020 in response to the coronavirus disease 2019 (COVID-19) pandemic, COVID-Net is a global open source, open access initiative dedicated to accelerating advancement in machine learning to aid front-line healthcare workers and clinical institutions around the world fighting the continuing pandemic. Towards this goal, our global multi-disciplinary team of researchers, developers, and clinicians have made publicly available a suite of tailored deep neural network models for tackling different challenges ranging from screening to risk stratification to treatment planning for patients with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Furthermore, we have made available fully curated, open access benchmark datasets comprised of some of the largest, most diverse patient cohorts from around the world. We hope the open-source, open-access release of COVID-Net deep learning models and associated large-scale benchmark datasets will motivate and enable researchers, clinicians, and citizen data scientists alike from around the world to build upon them and accelerate progress in this field. We continue to regularly release new models and benchmark datasets to keep up with the dynamic nature of the evolving pandemic, and have since expanded the initiative with the open source TB-Net initiative for tuberculosis screening, Fibrosis-Net initiative for pulmonary fibrosis progression prediction, and Cancer-Net initiative for cancer screening.


------- Courtesy Our World in Data

Benchmark Dataset Status:


Core COVID-Net Team

Project Lead: Alexander Wong (