Meta Learning With Medical Imaging and Health Informatics Applications
Aus der Reihe
MICCAI Society book
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- Taschenbuch ausgewählt
- eBook
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Sprache:Englisch
Fr. 146.00
inkl. gesetzl. MwSt.,
Beschreibung
Produktdetails
Einband
Taschenbuch
Erscheinungsdatum
29.09.2022
Abbildungen
60 illustrations (20 in full color)
Herausgeber
Hien Van Nguyen + weitereVerlag
Elsevier Science & TechnologySeitenzahl
428
Maße (L/B/H)
19.2/23.6/2.7 cm
Gewicht
906 g
Sprache
Englisch
ISBN
978-0-323-99851-2
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.
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