Trustworthy AI in Medical Imaging

Section 1 – Robustness 1. Machine Learning Robustness: A Primer 2. Navigating the Unknown: Out-of-Distribution Detection for Medical Imaging 3. From Out-of-Distribution Detection and Uncertainty Quantification to Quality Control 4. Domain shift, Domain Adaptation and Generalization Section 2 - Validation, Transparency and Reproducibility 5. Fundamentals on Transparency, Reproducibility and Validation 6. Reproducibility in Medical Image Computing 7. Collaborative Validation and Performance Assessment in Medical Imaging Applications 8. Challenges as a Framework for Trustworthy AI Section 3 – Bias and Fairness 9. Bias and Fairness 10. Open Challenges on Fairness of Artificial Intelligence in Medical Imaging Applications Section 4 - Explainability, Interpretability and Causality 11. Fundamentals on Explainable and Interpretable Artificial Intelligence Models 12. Causality: Fundamental Principles and Tools 13. Interpretable AI for Medical Image Analysis: Methods, Evaluation and Clinical Considerations 14. Explainable AI for Medical Image Analysis 15. Causal Reasoning in Medical Imaging Section 5 - Privacy-preserving ML 16. Fundamentals of Privacy-Preserving and Secure Machine Learning 17. Differential Privacy in Medical Imaging Applications Section 6 - Collaborative Learning 18. Fundamentals on Collaborative Learning 19. Large-scale Collaborative Studies in Medical Imaging through Meta Analyses 20. Promises and Open Challenges for Translating Federated learning in Hospital Environments Olivier Humbert, Hugo Crochet, and Renaud SchiappaSection 7 - Beyond the Technical Aspects 21. Stakeholder Engagement: The Path to Trustworthy AI in Healthcare