A Workshop on Medical AI
Level up your ML skills with a hands-on dive into medical AI at our 2nd UofT AI Workshop. In just one hour, you will go from raw medical images to training and visualizing your own model’s results.
About the workshop
This session introduces how AI is used in healthcare, with a focus on real-world medical imaging workflows and challenges. You will work directly with a medical image dataset and see how models learn to detect and segment patterns in these images.
What you’ll learn
Core vision architectures used in medical AI, including CNNs and U-Net-style models, and why they are so effective for imaging tasks.
Practical parameter tuning: how choices like learning rate, batch size, and epochs impact performance, and how to iterate efficiently.
How to visualize model outputs (e.g., overlays, masks, and error cases) so you can interpret and communicate results clearly.
Format and prerequisites
This is a 1-hour, in-person, beginner-friendly technical workshop running Friday, Nov 28, 5–6 pm at BA B024 on the UofT St. George campus. Basic Python familiarity is helpful but not strictly required; you will be guided through prepared notebooks so you can focus on concepts and experimentation.
Who should attend
Students curious about medical AI, computer vision, or applying ML to real-world impact areas like healthcare.
Beginners who have seen ML in theory and want a structured, hands-on first project with support.
Anyone in UofT AI interested in building portfolio-ready projects and getting more involved with our workshop series.