Trustworthy AI in Medical Imaging brings together scientific researchers, medical experts, and industry partners working in the field of trustworthiness, bridging the gap between AI research and concrete medical applications and making it a learning resource for undergraduates, masters students, and researchers in AI for medical imaging applications. The book will help readers acquire the basic notions of AI trustworthiness and understand its concrete application in medical imaging, identify pain points and solutions to enhance trustworthiness in medical imaging applications, understand current limitations and perspectives of trustworthy AI in medical imaging, and identify novel research directions. Although the problem of trustworthiness in AI is actively researched in different disciplines, the adoption and implementation of trustworthy AI principles in real-world scenarios is still at its infancy. This is particularly true in medical imaging where guidelines and standards for trustworthiness are critical for the successful deployment in clinical practice. After setting out the technical and clinical challenges of AI trustworthiness, the book gives a concise overview of the basic concepts before presenting state-of-the-art methods for solving these challenges. Introduces the key concepts of trustworthiness in AI.Presents state-of-the-art methodologies for trustworthy AI in medical imaging.Outlines major initiatives focusing on real-world deployment of trustworthy principles in medical imaging applications.Presents outstanding questions still to be solved and discusses future research directions. INDICE: 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
- ISBN: 978-0-443-23761-4
- Editorial: Academic Press
- Encuadernacion: Rústica
- Páginas: 455
- Fecha Publicación: 01/12/2024
- Nº Volúmenes: 1
- Idioma: Inglés