Tutorials

This tutorial provides an introduction to the validation of AI methods in medical image analysis. Reliable validation is essential to decide whether algorithms are working as intended beyond standard leaderboards, and even whether they have a potential for clinical translation. It ensures that model performance estimates are meaningful, reproducible, and reflecting the underlying research questions. The course introduces key principles and practical techniques for validating AI models. Participants will learn how to select and interpret appropriate performance metrics, quantify model performance uncertainty, and assess the robustness of rankings in algorithm benchmarking. The tutorial combines theoretical lectures with guided hands-on exercises.

Preliminary schedule:

09:00 - 10:30 | Performance Metrics

• • • • Introduction to performance metrics for classification and segmentation tasks
      • Common pitfalls and limitations of performance metrics
      • Selecting appropriate metrics based on the research question
      • Hands-on exercises with practical use cases

10:30-11:00  | Coffee break

11:00 - 12:30 | Uncertainty and Model Performance Reporting

• • • • Reporting variability and uncertainty in model performance estimates
      • Confidence interval calculation and model comparisons
      • Assessing the validity of model outperformance claims in scientific studies
      • Good practices for reporting AI model performance
      • Hands-on exercises with practical use cases