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Information Processing in Medical Imaging - 27th International Conference, IPMI 2021, Virtual Event, June 28-June 30, 2021, Proceedings (Paperback, 1st ed. 2021)
Aasa Feragen, Stefan Sommer, Julia Schnabel, Mads Nielsen
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R3,372
Discovery Miles 33 720
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Ships in 10 - 15 working days
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This book constitutes the proceedings of the 27th International
Conference on Information Processing in Medical Imaging, IPMI 2021,
which was held online during June 28-30, 2021. The conference was
originally planned to take place in Bornholm, Denmark, but changed
to a virtual format due to the COVID-19 pandemic. The 59 full
papers presented in this volume were carefully reviewed and
selected from 200 submissions. They were organized in topical
sections as follows: registration; causal models and
interpretability; generative modelling; shape; brain connectivity;
representation learning; segmentation; sequential modelling;
learning with few or low quality labels; uncertainty quantification
and generative modelling; and deep learning.
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Biomedical Image Registration - 8th International Workshop, WBIR 2018, Leiden, The Netherlands, June 28-29, 2018, Proceedings (Paperback, 1st ed. 2018)
Stefan Klein, Marius Staring, Stanley Durrleman, Stefan Sommer
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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 8th
International Workshop on Biomedical Image Registration, WBIR 2018,
held in Leiden, The Netherlands, in June 2018. The 11 full and
poster papers included in this volume were carefully reviewed and
selected from 17 submitted papers. The papers are organized in the
following topical sections: Sliding Motion, Groupwise Registration,
Acceleration, and Applications and Evaluation.
Nicht erst die Coronakrise zeigt, dass die Chancen der
Digitalisierung von deutschen Unternehmen noch zu wenig genutzt und
Kompetenzen dafur zu wenig aktiviert werden - auch wegen der hohen
Unsicherheit im UEbergang zu einer neuen stabilen
Branchen-architektur. Dieses Buch zeigt die Chancen der
Digitalisierung, analysiert branchen-ubergreifend den Status Quo
deutscher Unternehmen und bietet Ansatzpunkte, wie sie die
Digitalisierung jetzt richtig nutzen und die digitale
Transformation beschleunigen koennen.
Over the past 15 years, there has been a growing need in the
medical image computing community for principled methods to process
nonlinear geometric data. Riemannian geometry has emerged as one of
the most powerful mathematical and computational frameworks for
analyzing such data. Riemannian Geometric Statistics in Medical
Image Analysis is a complete reference on statistics on Riemannian
manifolds and more general nonlinear spaces with applications in
medical image analysis. It provides an introduction to the core
methodology followed by a presentation of state-of-the-art methods.
Beyond medical image computing, the methods described in this book
may also apply to other domains such as signal processing, computer
vision, geometric deep learning, and other domains where statistics
on geometric features appear. As such, the presented core
methodology takes its place in the field of geometric statistics,
the statistical analysis of data being elements of nonlinear
geometric spaces. The foundational material and the advanced
techniques presented in the later parts of the book can be useful
in domains outside medical imaging and present important
applications of geometric statistics methodology Content includes:
The foundations of Riemannian geometric methods for statistics on
manifolds with emphasis on concepts rather than on proofs
Applications of statistics on manifolds and shape spaces in medical
image computing Diffeomorphic deformations and their applications
As the methods described apply to domains such as signal processing
(radar signal processing and brain computer interaction), computer
vision (object and face recognition), and other domains where
statistics of geometric features appear, this book is suitable for
researchers and graduate students in medical imaging, engineering
and computer science.
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