Trustworthy AI in Medical Imaging

Marco Lorenzi is a tenured research scientist at the Inria Center of University Côte d’Azur (France), and junior chair holder at the Interdisciplinary Institute for Artificial Intelligence 3IA Côte d’Azur. He is also a visiting Senior Lecturer at the School of Biomedical Engineering & Imaging Sciences at King’s College London. His research focuses on developing statistical learning methods to model heterogeneous and secured data in biomedical applications. He is the founder and scientific responsible for the open-source federated learning platform Fed-BioMed. Dr Zuluaga is an assistant professor in the Data Science department at EURECOM. She holds a junior chair at the 3IA Institute Côte d’Azur and is a visiting Senior Lecturer within the School of Biomedical Engineering & Imaging Sciences at King’s College London. Her current research focuses on the development of machine learning techniques that can be safely deployed in high risk domains, such as healthcare, by addressing data complexity, low tolerance to errors and poor reproducibility.

Preface Section 1- Preliminaries

Introduction to Trustworthy AI for Medical Imaging & Lecture Plan
The fundamentals of AI ethics in Medical Imaging

Section 2– Robustness

3. Machine Learning Robustness: A Primer 4. Navigating the Unknown: Out-of-Distribution Detection for Medical Imaging 5. From Out-of-Distribution Detection and Uncertainty Quantification to Quality Control 6. Domain shift, Domain Adaptation and GeneralizationSection 3 - Validation, Transparency and Reproducibility 7. Fundamentals on Transparency, Reproducibility and Validation 8. Reproducibility in Medical Image Computing 9. Collaborative Validation and Performance Assessment in Medical Imaging Applications 10. Challenges as a Framework for Trustworthy AI Section 4 – Bias and Fairness 11. Bias and Fairness 12. Open Challenges on Fairness of Artificial Intelligence in Medical Imaging Applications Section 5 - Explainability, Interpretability and Causality 13. Fundamentals on Explainable and Interpretable Artificial Intelligence Models 14. Causality: Fundamental Principles and Tools 15. Interpretable AI for Medical Image Analysis: Methods, Evaluation and Clinical Considerations 16. Explainable AI for Medical Image Analysis 17. Causal Reasoning in Medical Imaging Section 6 - Privacy-preserving ML 18. Fundamentals of Privacy-Preserving and Secure Machine Learning 19. Differential Privacy in Medical Imaging Applications Section 7 - Collaborative Learning 20. Fundamentals on Collaborative Learning 21. Large-scale Collaborative Studies in Medical Imaging through Meta Analyses 22. Promises and Open Challenges for Translating Federated learning in Hospital Environments Section 8 - Beyond the Technical Aspects 23. Stakeholder Engagement: The Path to Trustworthy AI in Healthcare