Meta Learning with Medical Imaging and Health Informatics Applications
| PUBLISHER | Academic Press (09/30/2022) |
| PRODUCT TYPE | Paperback (Paperback) |
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.
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Product Format
Product Details
ISBN-13:
9780323998512
ISBN-10:
0323998518
Binding:
Paperback or Softback (Trade Paperback (Us))
Content Language:
English
More Product Details
Page Count:
428
Carton Quantity:
9
Product Dimensions:
7.50 x 0.87 x 9.25 inches
Weight:
1.62 pound(s)
Feature Codes:
Bibliography,
Index,
Illustrated
Country of Origin:
US
Subject Information
BISAC Categories
Computers | Image Processing
Dewey Decimal:
006.31
Library of Congress Control Number:
2023277803
Descriptions, Reviews, Etc.
publisher marketing
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.
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Your Price
$128.70