Meta-Learning, or learning to learn, has become increasingly popular in recent years. Instead of building AI systems from scratch for each machine learning task, Meta-Learning constructs computational mechanisms to systematically and efficiently adapt to new tasks. The meta-learning paradigm has great potential to address deep neural networks’ fundamental challenges such as intensive data requirement, computationally expensive training, and limited capacity for transfer among tasks.
This book provides a concise summary of Meta-Learning theories and their diverse applications in medical imaging and health informatics. It covers the unifying theory of meta-learning and its popular variants such as model-agnostic learning, memory augmentation, prototypical networks, and learning to optimize. The book brings together thought leaders from both machine learning and health informatics fields to discuss the current state of Meta-Learning, its relevance to medical imaging and health informatics, and future directions. The book comes with a GitHub repository consisting of various code examples and documentation to help the audience to set up Meta-Learning algorithms for their applications quickly.
1. Chapter 2 Introduction to meta learning - p. 27 2. Chapter 3 Metric learning algorithms for meta learning - p. 37 3. Chapter 4 Meta learning by optimization - p. 53 4. Chapter 5 Model-based meta learning - p. 65 5. Chapter 6 Meta learning for domain generalization - p. 75 6. Chapter 7 Few-shot chest x-ray diagnosis using discriminative ensemble learning - p. 89 7. Chapter 8 Domain generalization of deep networks for medical image segmentation via meta learning - p. 117 8. Chapter 9 Meta learning for adaptable lung nodule image analysis - p. 141 9. Chapter 10 Few-shot segmentation of 3D medical images - p. 161 10. Chapter 11 Smart task design for meta learning medical image analysis systems - p. 185 11. Chapter 12 AGILE - a meta learning framework for few-shot brain cell classification - p. 213 12. Chapter 13 Few-shot learning for dermatological disease diagnosis - p. 235 13. Chapter 14 Knowledge-guided meta learning for disease prediction - p. 253 14. Chapter 15 Case study: few-shot pill recognition - p. 275 15. Chapter 16 Meta learning for anomaly detection in fundus photographs - p. 301 16. Chapter 17 Rare disease classification via difficulty-aware meta learning - p. 331 17. Chapter 18 Improved MR image reconstruction using federated learning - p. 351 18. Chapter 19 Neural architecture search for medical image applications - p. 369 19. Chapter 20 Meta learning in the big data regime - p. 385 20. Index - p. 395 21. Index - Continued - p. 406
Meta-Learning, or learning to learn, has become increasingly popular in recent years. Instead of building AI systems from scratch for each machine learning task, Meta-Learning constructs computational mechanisms to systematically and efficiently adapt to new tasks. The meta-learning paradigm has great potential to address deep neural networks’ fundamental challenges such as intensive data requirement, computationally expensive training, and limited capacity for transfer among tasks.
This book provides a concise summary of Meta-Learning theories and their diverse applications in medical imaging and health informatics. It covers the unifying theory of meta-learning and its popular variants such as model-agnostic learning, memory augmentation, prototypical networks, and learning to optimize. The book brings together thought leaders from both machine learning and health informatics fields to discuss the current state of Meta-Learning, its relevance to medical imaging and health informatics, and future directions. The book comes with a GitHub repository consisting of various code examples and documentation to help the audience to set up Meta-Learning algorithms for their applications quickly.
1. Chapter 2 Introduction to meta learning - p. 27 2. Chapter 3 Metric learning algorithms for meta learning - p. 37 3. Chapter 4 Meta learning by optimization - p. 53 4. Chapter 5 Model-based meta learning - p. 65 5. Chapter 6 Meta learning for domain generalization - p. 75 6. Chapter 7 Few-shot chest x-ray diagnosis using discriminative ensemble learning - p. 89 7. Chapter 8 Domain generalization of deep networks for medical image segmentation via meta learning - p. 117 8. Chapter 9 Meta learning for adaptable lung nodule image analysis - p. 141 9. Chapter 10 Few-shot segmentation of 3D medical images - p. 161 10. Chapter 11 Smart task design for meta learning medical image analysis systems - p. 185 11. Chapter 12 AGILE - a meta learning framework for few-shot brain cell classification - p. 213 12. Chapter 13 Few-shot learning for dermatological disease diagnosis - p. 235 13. Chapter 14 Knowledge-guided meta learning for disease prediction - p. 253 14. Chapter 15 Case study: few-shot pill recognition - p. 275 15. Chapter 16 Meta learning for anomaly detection in fundus photographs - p. 301 16. Chapter 17 Rare disease classification via difficulty-aware meta learning - p. 331 17. Chapter 18 Improved MR image reconstruction using federated learning - p. 351 18. Chapter 19 Neural architecture search for medical image applications - p. 369 19. Chapter 20 Meta learning in the big data regime - p. 385 20. Index - p. 395 21. Index - Continued - p. 406
