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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