Meta Learning With Medical Imaging and Health Informatics Applications
Aus der Reihe
The MICCAI Society book Series
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- Taschenbuch
- eBook ausgewählt
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Form:Einzelkauf Download
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Sprache:Englisch
113,00 €
inkl. gesetzl. MwSt.Beschreibung
Produktdetails
Format
ePUB 3
Kopierschutz
Nein
Family Sharing
Nein
Text-to-Speech
Ja
Erscheinungsdatum
24.09.2022
Herausgeber
Hien van Nguyen + weitereVerlag
Elsevier Science & Techn.Seitenzahl
428 (Printausgabe)
Dateigröße
34287 KB
Sprache
Englisch
EAN
9780323998529
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.
- 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
- 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
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