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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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Interpretable and Annotation-Efficient Learning for Medical Image Computing - Third International Workshop, iMIMIC 2020, Second International Workshop, MIL3ID 2020, and 5th International Workshop, LABELS 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020, Proceedings (Paperback, 1st ed. 2020)
Jaime Cardoso, Hien Van Nguyen, Nicholas Heller, Pedro Henriques Abreu, Ivana Isgum, …
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Discovery Miles 15 640
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Ships in 10 - 15 working days
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This book constitutes the refereed joint proceedings of the Third
International Workshop on Interpretability of Machine Intelligence
in Medical Image Computing, iMIMIC 2020, the Second International
Workshop on Medical Image Learning with Less Labels and Imperfect
Data, MIL3ID 2020, and the 5th International Workshop on
Large-scale Annotation of Biomedical data and Expert Label
Synthesis, LABELS 2020, held in conjunction with the 23rd
International Conference on Medical Imaging and Computer-Assisted
Intervention, MICCAI 2020, in Lima, Peru, in October 2020. The 8
full papers presented at iMIMIC 2020, 11 full papers to MIL3ID
2020, and the 10 full papers presented at LABELS 2020 were
carefully reviewed and selected from 16 submissions to iMIMIC, 28
to MIL3ID, and 12 submissions to LABELS. The iMIMIC papers focus on
introducing the challenges and opportunities related to the topic
of interpretability of machine learning systems in the context of
medical imaging and computer assisted intervention. MIL3ID deals
with best practices in medical image learning with label scarcity
and data imperfection. The LABELS papers present a variety of
approaches for dealing with a limited number of labels, from
semi-supervised learning to crowdsourcing.
A Healer's Journey relates Master Hien's personal and actual
accounts of spiritual encounters, demon battles, hauntings, and
other displays of supernatural power while explaining the
distinction between the various types of magic practitioners and
their relative status or rank to each other. His stories will test
your boundaries of reality, for they seem to be lifted from
fictional works or movie sets. But the stories are real and require
the reader to embrace the existence of a spiritual world linked to
our own. The author also reveals the grueling commitment demanded
of a pupil wishing to become a true practitioner in the art of
magic, as the author himself has had extensive first-hand
experience.
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