Skip to the content.


Cancer-Net Open Initiative

Cancer-Net is a global open source, open access initiative dedicated to accelerating advancement in machine learning to aid clinicians in the fight against cancer. 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 various forms of cancer. Furthermore, we have made available fully curated, open access benchmark datasets comprised of the largest, most diverse patient cohorts from around the world for correlated diffusion imaging, a medical imaging modality tailored for cancer imaging. We hope the open-source, open-access release of Cancer-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 lastest developments in cancer research. Cancer-Net is a joint initiative with the COVID-Net initiative and the GenAI4Good initiative.



Benchmark Dataset Status:


Core Cancer-Net Team

Project Lead: Alexander Wong (