Meta Learning With Medical Imaging and Health Informatics Applications

A great resource to learn Meta-learning methods and their application to Medical Imaging and Health Informatics

Hien Van Nguyen (Edited by), Ronald Summers (Edited by), Rama Chellappa (Edited by)

9780323998512, Elsevier Science

Paperback / softback, published 29 September 2022

428 pages, 60 illustrations (20 in full color)
23.5 x 19 x 2.7 cm, 0.45 kg

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.

1. Meta-Learning Theory
2. Meta-Learning for Medical Image Detection and Segmentation
3. Meta-Learning for Medical Image Diagnosis
4. Meta-Learning for Other Biomedical Applications
5. Meta-Learning for Health Informatics

Subject Areas: Image processing [UYT]