Meta Learning With Medical Imaging and Health Informatics Applications

Hien Van Nguyen, Ronald Summers, Rama Chellappa

• Publisher: Academic Press
• Year: 2022
• Print ISBN: 9780323998512
• eBook ISBN: 9780323998529
• Topic: Computer Science
• Subtopic: Digital Media
• Index: Computer Science

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. - First book on applying Meta Learning to medical imaging - Pioneers in the field as contributing authors to explain the theory and its development - Has GitHub repository consisting of various code examples and documentation to help the audience to set up Meta-Learning algorithms for their applications quickly

Table of contents

• Meta Learning With Medical Imaging and Health Informatics Applications
• Cover
• Title Page
• Copyright
• Table of Contents
• Contributors
• Chapter 1 Learning to learn in medical applications
• Chapter 2 Introduction to meta learning
• Chapter 3 Metric learning algorithms for meta learning
• Chapter 4 Meta learning by optimization
• Chapter 5 Model-based meta learning
• Chapter 6 Meta learning for domain generalization
• Chapter 7 Few-shot chest x-ray diagnosis using discriminative ensemble learning
• Chapter 8 Domain generalization of deep networks for medical image segmentation via meta learning
• Chapter 9 Meta learning for adaptable lung nodule image analysis
• Chapter 10 Few-shot segmentation of 3D medical images
• Chapter 11 Smart task design for meta learning medical image analysis systems
• Chapter 12 AGILE - a meta learning framework for few-shot brain cell classification
• Chapter 13 Few-shot learning for dermatological disease diagnosis
• Chapter 14 Knowledge-guided meta learning for disease prediction
• Chapter 15 Case study: few-shot pill recognition
• Chapter 16 Meta learning for anomaly detection in fundus photographs
• Chapter 17 Rare disease classification via difficulty-aware meta learning
• Chapter 18 Improved MR image reconstruction using federated learning
• Chapter 19 Neural architecture search for medical image applications
• Chapter 20 Meta learning in the big data regime
• Index