Meta Learning With Medical Imaging and Health Informatics Applications
Häftad, Engelska, 2022
Av Hien Van Nguyen, Ronald Summers, Rama Chellappa, USA) Nguyen, Hien Van (Assistant Professor, Department of Electrical and Computer Engineering Department, University of Houston, USA) Summers, Ronald (University of Michigan, Ann Arbor, MI, USA) Chellappa, Rama (University of Maryland, College Park, MD, Hien van Nguyen
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Fri frakt för medlemmar vid köp för minst 249 kr.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.
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.
- First book on applying Meta Learning to medical imaging
- Pioneers in the field as contributing authors to explain the theory and its development
- Has GitHub repository consisting of various code examples and documentation to help the audience to set up Meta-Learning algorithms for their applications quickly
Produktinformation
- Utgivningsdatum2022-09-29
- Mått191 x 235 x 27 mm
- Vikt870 g
- FormatHäftad
- SpråkEngelska
- SerieThe MICCAI Society book Series
- Antal sidor428
- FörlagElsevier Science
- ISBN9780323998512