Applications of Artificial Intelligence in Oncology

Image segmentation to develop radiation treatment in head and neck cancers is a time-consuming, manual task taking several hours per patient. To tackle this issue, CDI partnered with the Vector Institute to launch an open competition to engage scientists, clinicians, and students to improve region of interest segmentation for radiation therapy.

The competition set out to explore the computational limitations of object detection and contouring with the goal of developing accurate auto-segmentation models in medical imaging. 11 teams of over 30 participants across UHN and the Vector Institute accepted this machine learning challenge and developed AI-assisted models by partnering clinical expertise with AI specialists.

“This was a successful challenge that targeted a wide variety of researcher, scientist, and student backgrounds within the UHN and Vector community,” said Ian Gormely, Communications Specialist at the Vector Institute. “We look forward to future CDI collaborations and AI-related projects to continue to help transform healthcare innovation.”

The first-place winner of the 2022 Machine Learning Challenge was the Fight Tumor team composed of Bo Wang (Lead), Jun Ma, Rex Ma, and Ronald Xie. Their winning model will ultimately improve workflow processes and create efficiencies that will allow patients to receive radiation treatment sooner. Their findings were presented at the 2022 Toronto Machine Learning Summit and will be published in an upcoming research paper. The data and computational models will also be made available to the biomedical community (Open Science).

11 teams formed

30 participants

2,700 images used to train autosegmenation models