Trustworthy AI in Medical Imaging
by Marco Lorenzi, Maria A Zuluaga
1st Edition
Publisher: Academic Press
Book Details
| Print ISBN | 9780443237614 |
| eText ISBN | 9780443237607 |
| Publisher | Academic Press |
| Publishing Year | 2024 |
| Edition | 1st Edition |
| Language | English |
| Pages | 536 |
The 1st Edition of Trustworthy AI in Medical Imaging provides a comprehensive academic textbook designed to address core technical and clinical challenges in diagnostic computing. Published in 2024 under Elsevier's Academic Press imprint and edited by Marco Lorenzi and Maria A. Zuluaga, this volume presents structured instructional material aimed at undergraduate students, master's degree candidates, and academic researchers.
The authors organize foundational theories and computational methods across several distinct thematic arcs. Early chapters introduce the fundamentals of artificial intelligence ethics, machine learning robustness, and mechanisms for out-of-distribution detection. The focus then transitions to technical strategies for handling domain shift, domain adaptation, and model generalization, while evaluating persistent open challenges involving bias and fairness in clinical data processing.
In its final section, the text identifies stakeholder engagement as an effective path toward achieving reliable healthcare deployments. This thematic design provides graduate seminars and university courses with a clear framework for analyzing dependable medical imaging systems.
Table of Contents
Chapter 1: Introduction to Trustworthy AI for Medical Imaging & Lecture Plan
Chapter 2: The fundamentals of AI ethics in Medical Imaging
Chapter 3: Machine Learning Robustness: A Primer
Chapter 4: Navigating the Unknown: Out-of-Distribution Detection for Medical Imaging
Chapter 5: From Out-of-Distribution Detection and Uncertainty Quantification to Quality Control
Chapter 6: Domain shift, Domain Adaptation and Generalization
Chapter 7: Fundamentals on Transparency, Reproducibility and Validation
Chapter 8: Reproducibility in Medical Image Computing
Chapter 9: Collaborative Validation and Performance Assessment in Medical Imaging Applications
Chapter 10: Challenges as a Framework for Trustworthy AI
Chapter 11: Bias and Fairness
Chapter 12: Open Challenges on Fairness of Artificial Intelligence in Medical Imaging Applications
Chapter 13: Fundamentals on Explainable and Interpretable Artificial Intelligence Models
Chapter 14: Causality: Fundamental Principles and Tools
Chapter 15: Interpretable AI for Medical Image Analysis: Methods, Evaluation and Clinical Considerations
Chapter 16: Explainable AI for Medical Image Analysis
Chapter 17: Causal Reasoning in Medical Imaging
Chapter 18: Fundamentals of Privacy-Preserving and Secure Machine Learning
Chapter 19: Differential Privacy in Medical Imaging Applications
Chapter 20: Fundamentals on Collaborative Learning
Chapter 21: Large-scale Collaborative Studies in Medical Imaging through Meta Analyses
Chapter 22: Promises and Open Challenges for Translating Federated learning in Hospital Environments
Chapter 23: Stakeholder Engagement: The Path to Trustworthy AI in Healthcare
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