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Meta Learning With Medical Imaging and Health Informatics Applications (The MICCAI Society book Series)

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

2 in stock

Ksh 23,750.00

Format: Paperback or Softback

ISBN-10: 0323998518

ISBN-13: 9780323998512

Collection / Series: The MICCAI Society book Series

Collection Type: Publisher collection

Publisher: Elsevier Science & Technology

Imprint: Academic Press Inc

Country of Manufacture: NL

Country of Publication: GB

Publication Date: Sep 29th, 2022

Publication Status: Active

Product extent: 428 Pages

Weight: 906.00 grams

Dimensions (height x width x thickness): 19.20 x 23.60 x 2.70 cms

Product Classification / Subject(s): Image processing

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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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