Trustworthy AI in Medical Imaging

Herausgeber: Lorenzi, Marco; A Zuluaga, Maria

• Broschiertes Buch

Produktbeschreibung

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.

---

Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.

Produktdetails

• Verlag: Elsevier Science
• Seitenzahl: 536
• Erscheinungstermin: 3. Dezember 2024
• Englisch
• Gewicht: 450g
• ISBN-13: 9780443237614
• ISBN-10: 0443237611
• Artikelnr.: 70071454

Inhaltsangabe

Preface Section 1- Preliminaries
1. Introduction to Trustworthy AI for Medical Imaging & Lecture Plan
2. 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 Generalization
Section 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