Trustworthy AI in Medical Imaging
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Trustworthy AI in Medical Imaging
Lorenzi, Marco; A Zuluaga, Maria
Elsevier Science Publishing Co Inc
12/2024
455
Mole
9780443237614
Pré-lançamento - envio 15 a 20 dias após a sua edição
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Preface
Preliminaries
Introduction to Trustworthy AI for Medical Imaging & Lecture Plan
The fundamentals of AI ethics in Medical Imaging
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
Section 7 - Beyond the Technical Aspects
21. Stakeholder Engagement: The Path to Trustworthy AI in Healthcare
Preliminaries
Introduction to Trustworthy AI for Medical Imaging & Lecture Plan
The fundamentals of AI ethics in Medical Imaging
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
Section 7 - Beyond the Technical Aspects
21. Stakeholder Engagement: The Path to Trustworthy AI in Healthcare
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Trustworthiness; Privacy-preserving Machine Learning; Data Governance; Fairness; Robustness; Explainability and interpretability; Ethics
Preface
Preliminaries
Introduction to Trustworthy AI for Medical Imaging & Lecture Plan
The fundamentals of AI ethics in Medical Imaging
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
Section 7 - Beyond the Technical Aspects
21. Stakeholder Engagement: The Path to Trustworthy AI in Healthcare
Preliminaries
Introduction to Trustworthy AI for Medical Imaging & Lecture Plan
The fundamentals of AI ethics in Medical Imaging
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
Section 7 - Beyond the Technical Aspects
21. Stakeholder Engagement: The Path to Trustworthy AI in Healthcare
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
