Meta Learning With Medical Imaging and Health Informatics Applications Paperback / softback
Edited by Hien Van (Assistant Professor, Department of Electrical and Computer Engineering Department, Nguyen, Ronald (University of Michigan, Ann Arbor, MI, USA) Summers, Rama (University of Maryland, College Park, MD, USA) Chellappa
Part of the The MICCAI Society book Series series
Paperback / softback
Description
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.
Information
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Only a few left - usually despatched within 24 hours
- Format:Paperback / softback
- Pages:428 pages, 60 illustrations (20 in full color); Illustrations, unspecified
- Publisher:Elsevier Science & Technology
- Publication Date:29/09/2022
- Category:
- ISBN:9780323998512
£99.95
£93.19
Information
-
Only a few left - usually despatched within 24 hours
- Format:Paperback / softback
- Pages:428 pages, 60 illustrations (20 in full color); Illustrations, unspecified
- Publisher:Elsevier Science & Technology
- Publication Date:29/09/2022
- Category:
- ISBN:9780323998512
