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.
1. Chapter 1: Introduction to trustworthy AI for medical imaging - p. 3 2. Chapter 2: The fundamentals of AI ethics in medical imaging - p. 7 3. Part 2: Robustness - p. 33 4. Chapter 3: Machine learning robustness: a primer - p. 37 5. Chapter 4: Navigating the unknown: out-of-distribution detection for medical imaging - p. 73 6. Chapter 5: From out-of-distribution detection to quality control - p. 101 7. Chapter 6: Domain shift, domain adaptation, and generalization - p. 127 8. Part 3: Validation, transparency, and reproducibility - p. 151 9. Chapter 7: Fundamentals on transparency, reproducibility, and validation - p. 155 10. Chapter 8: Reproducibility in medical image computing: what is it and how is it assessed? - p. 177 11. Chapter 9: Collaborative evaluation for performance assessment of medical imaging applications - p. 205 12. Chapter 10: Challenges as a framework for trustworthy AI - p. 223 13. Part 4: Bias and fairness - p. 243 14. Chapter 11: Bias and fairness - p. 247 15. Chapter 12: Open challenges on fairness of artificial intelligence in medical imaging applications - p. 265 16. Part 5: Explainability, interpretability, and causality - p. 276 17. Chapter 13: Fundamentals on explainable and interpretable artificial intelligence models - p. 279 18. Chapter 14: Causality: fundamental principles and tools - p. 297 19. Chapter 15: Interpretable AI for medical image analysis: methods, evaluation, and clinical considerations - p. 315 20. Chapter 16: Explainable AI for medical image analysis - p. 347 21. Chapter 17: Causal reasoning in medical imaging - p. 367 22. Part 6: Privacy-preserving machine learning - p. 381 23. Chapter 18: Fundamentals of privacy-preserving and secure machine learning - p. 385 24. Chapter 19: Differential privacy in medical imaging applications - p. 411 25. Part 7: Collaborative learning - p. 424 26. Chapter 20: Collaborative learning - p. 427 27. Chapter 21: Large-scale collaborative studies in medical imaging through meta-analyses - p. 441 28. Chapter 22: Promises and open challenges for translating federated learning in hospital environments - p. 455 29. Part 8: Beyond the technical aspects - p. 467 30. Chapter 23: Stakeholder engagement: the path to trustworthy AI in healthcare - p. 471 31. Index - p. 495
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.
1. Chapter 1: Introduction to trustworthy AI for medical imaging - p. 3 2. Chapter 2: The fundamentals of AI ethics in medical imaging - p. 7 3. Part 2: Robustness - p. 33 4. Chapter 3: Machine learning robustness: a primer - p. 37 5. Chapter 4: Navigating the unknown: out-of-distribution detection for medical imaging - p. 73 6. Chapter 5: From out-of-distribution detection to quality control - p. 101 7. Chapter 6: Domain shift, domain adaptation, and generalization - p. 127 8. Part 3: Validation, transparency, and reproducibility - p. 151 9. Chapter 7: Fundamentals on transparency, reproducibility, and validation - p. 155 10. Chapter 8: Reproducibility in medical image computing: what is it and how is it assessed? - p. 177 11. Chapter 9: Collaborative evaluation for performance assessment of medical imaging applications - p. 205 12. Chapter 10: Challenges as a framework for trustworthy AI - p. 223 13. Part 4: Bias and fairness - p. 243 14. Chapter 11: Bias and fairness - p. 247 15. Chapter 12: Open challenges on fairness of artificial intelligence in medical imaging applications - p. 265 16. Part 5: Explainability, interpretability, and causality - p. 276 17. Chapter 13: Fundamentals on explainable and interpretable artificial intelligence models - p. 279 18. Chapter 14: Causality: fundamental principles and tools - p. 297 19. Chapter 15: Interpretable AI for medical image analysis: methods, evaluation, and clinical considerations - p. 315 20. Chapter 16: Explainable AI for medical image analysis - p. 347 21. Chapter 17: Causal reasoning in medical imaging - p. 367 22. Part 6: Privacy-preserving machine learning - p. 381 23. Chapter 18: Fundamentals of privacy-preserving and secure machine learning - p. 385 24. Chapter 19: Differential privacy in medical imaging applications - p. 411 25. Part 7: Collaborative learning - p. 424 26. Chapter 20: Collaborative learning - p. 427 27. Chapter 21: Large-scale collaborative studies in medical imaging through meta-analyses - p. 441 28. Chapter 22: Promises and open challenges for translating federated learning in hospital environments - p. 455 29. Part 8: Beyond the technical aspects - p. 467 30. Chapter 23: Stakeholder engagement: the path to trustworthy AI in healthcare - p. 471 31. Index - p. 495
