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Trustworthy AI in Medical imaging

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Author:   Marco Lorenzi (Tenured Research Scientist, EPIONE team of Inria Sophia Antipolis and Université Côte d’Azur, Cedex, France) ,  Maria A Zuluaga (Assistant Professor, Data Science department, EURECOM, Biot, France)
Publisher:   Elsevier Science Publishing Co Inc
Imprint:   Academic Press Inc
Weight:   1.110kg
ISBN:  

9780443237614

ISBN 10:   0443237611
Pages:   536
Publication Date:   03 December 2024
Audience:   College/higher education ,  Tertiary & Higher Education
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand  
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Table of Contents

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

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Author Information

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.

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