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Showing 1 - 5 of
5 matches in All Departments
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Multiscale Multimodal Medical Imaging - Third International Workshop, MMMI 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings (Paperback, 1st ed. 2022)
Xiang Li, Jinglei Lv, Yuankai Huo, Bin Dong, Richard M. Leahy, …
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R1,636
Discovery Miles 16 360
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Ships in 10 - 15 working days
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This book constitutes the refereed proceedings of the Third
International Workshop on Multiscale Multimodal Medical Imaging,
MMMI 2022, held in conjunction with MICCAI 2022 in singapore, in
September 2022.The 12 papers presented were carefully reviewed and
selected from 18 submissions. The MMMI workshop aims to advance the
state of the art in multi-scale multi-modal medical imaging,
including algorithm development, implementation of methodology, and
experimental studies. The papers focus on medical image analysis
and machine learning, especially on machine learning methods for
data fusion and multi-score learning.
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Multimodal Learning for Clinical Decision Support - 11th International Workshop, ML-CDS 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings (Paperback, 1st ed. 2021)
Tanveer Syeda-Mahmood, Xiang Li, Anant Madabhushi, Hayit Greenspan, Quanzheng Li, …
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R1,632
Discovery Miles 16 320
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Ships in 10 - 15 working days
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This book constitutes the refereed joint proceedings of the 11th
International Workshop on Multimodal Learning for Clinical Decision
Support, ML-CDS 2021, held in conjunction with the 24th
International Conference on Medical Imaging and Computer-Assisted
Intervention, MICCAI 2021, in Strasbourg, France, in October 2021.
The workshop was held virtually due to the COVID-19 pandemic.The 10
full papers presented at ML-CDS 2021 were carefully reviewed and
selected from numerous submissions. The ML-CDS papers discuss
machine learning on multimodal data sets for clinical decision
support and treatment planning.
The SpringerBrief introduces FasTensor, a powerful parallel data
programming model developed for big data applications. This book
also provides a user's guide for installing and using FasTensor.
FasTensor enables users to easily express many data analysis
operations, which may come from neural networks, scientific
computing, or queries from traditional database management systems
(DBMS). FasTensor frees users from all underlying and tedious data
management tasks, such as data partitioning, communication, and
parallel execution. This SpringerBrief gives a high-level overview
of the state-of-the-art in parallel data programming model and a
motivation for the design of FasTensor. It illustrates the
FasTensor application programming interface (API) with an abundance
of examples and two real use cases from cutting edge scientific
applications. FasTensor can achieve multiple orders of magnitude
speedup over Spark and other peer systems in executing big data
analysis operations. FasTensor makes programming for data analysis
operations at large scale on supercomputers as productively and
efficiently as possible. A complete reference of FasTensor includes
its theoretical foundations, C++ implementation, and usage in
applications. Scientists in domains such as physical and
geosciences, who analyze large amounts of data will want to
purchase this SpringerBrief. Data engineers who design and develop
data analysis software and data scientists, and who use Spark or
TensorFlow to perform data analyses, such as training a deep neural
network will also find this SpringerBrief useful as a reference
tool.
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Multiscale Multimodal Medical Imaging - First International Workshop, MMMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings (Paperback, 1st ed. 2020)
Quanzheng Li, Richard Leahy, Bin Dong, Xiang Li
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R1,557
Discovery Miles 15 570
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Ships in 10 - 15 working days
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This book constitutes the refereed proceedings of the First
International Workshop on Multiscale Multimodal Medical Imaging,
MMMI 2019, held in conjunction with MICCAI 2019 in Shenzhen, China,
in October 2019. The 13 papers presented were carefully reviewed
and selected from 18 submissions. The MMMI workshop aims to advance
the state of the art in multi-scale multi-modal medical imaging,
including algorithm development, implementation of methodology, and
experimental studies. The papers focus on medical image analysis
and machine learning, especially on machine learning methods for
data fusion and multi-score learning.
The book systematically introduces the basic contents of data
science, including data preprocessing and basic methods of data
analysis, handling special problems (e.g. text analysis), deep
learning, and distributed systems.In addition to systematically
introducing the basic content of data science from a theoretical
point of view, the book also provides a large number of data
analysis practice cases.
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