Skip to main content

Meta Learning With Medical Imaging and Health Informatics Applications

  • 1st Edition - September 24, 2022
  • Editors: Hien Van Nguyen, Ronald Summers, Rama Chellappa
  • Language: English
  • Paperback ISBN:
    9 7 8 - 0 - 3 2 3 - 9 9 8 5 1 - 2
  • eBook ISBN:
    9 7 8 - 0 - 3 2 3 - 9 9 8 5 2 - 9

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,… Read more

Meta Learning With Medical Imaging and Health Informatics Applications

Purchase options

Limited Offer

Save 50% on book bundles

Immediately download your ebook while waiting for your print delivery. No promo code is needed.

Book bundle cover eBook and print

Institutional subscription on ScienceDirect

Request a sales quote
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.