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Showing 1 - 8 of
8 matches in All Departments
Total Knee Arthroplasty: Medical and Biomedical Engineering and
Science Concepts provides an extensive overview of the most recent
advancements in total knee arthroplasty (TKA) through a thorough
review of the literature in medicine, engineering, and technology.
Coverage includes the most recent engineering and computing
techniques, such as robotics, biomechanics, artificial intelligence
(AI), deep learning (DL), machine learning (ML), and optimization,
as well as the medical and surgical aspects of pre-existing
conditions, surgical procedure types, surgical complications,
patient care, and psychological factors. This book will be a
valuable introduction to TKA concepts and advances for academics,
students, and researchers.
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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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R1,475
Discovery Miles 14 750
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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.
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Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data - First MICCAI Workshop, DART 2019, and First International Workshop, MIL3ID 2019, Shenzhen, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019, Proceedings (Paperback, 1st ed. 2019)
Qian Wang, Fausto Milletari, Hien V. Nguyen, Shadi Albarqouni, M. Jorge Cardoso, …
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R1,469
Discovery Miles 14 690
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Ships in 10 - 15 working days
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This book constitutes the refereed proceedings of the First MICCAI
Workshop on Domain Adaptation and Representation Transfer, DART
2019, and the First International Workshop on Medical Image
Learning with Less Labels and Imperfect Data, MIL3ID 2019, held in
conjunction with MICCAI 2019, in Shenzhen, China, in October 2019.
DART 2019 accepted 12 papers for publication out of 18 submissions.
The papers deal with methodological advancements and ideas that can
improve the applicability of machine learning and deep learning
approaches to clinical settings by making them robust and
consistent across different domains. MIL3ID accepted 16 papers out
of 43 submissions for publication, dealing with best practices in
medical image learning with label scarcity and data imperfection.
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