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Titlebook: Machine Learning Meets Medical Imaging; First International Kanwal Bhatia,Herve Lombaert Conference proceedings 2015 Springer Internationa

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书目名称Machine Learning Meets Medical Imaging
副标题First International
编辑Kanwal Bhatia,Herve Lombaert
视频video
概述Includes supplementary material:
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Machine Learning Meets Medical Imaging; First International  Kanwal Bhatia,Herve Lombaert Conference proceedings 2015 Springer Internationa
描述. Normal0falsefalsefalseEN-USX-NONEX-NONE . /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;}This book constitutes the revised selected papers of theFirst International Workshop on Machine Learning in Medical Imaging, MLMMI2015, held in July 2015 in Lille, France, in conjunction with the 32ndInternational Conference on Machine Learning, ICML 2015...The 10 papers presented in this volume were carefullyreviewed and selected for inclusion in the book. The papers communicate thespecific needs and nuances of medical imaging to the machine learning communitywhile exposing
出版日期Conference proceedings 2015
关键词bioinformatics; computational biology; computer vision; machine learning; mathematical analysis; Alzheime
版次1
doihttps://doi.org/10.1007/978-3-319-27929-9
isbn_softcover978-3-319-27928-2
isbn_ebook978-3-319-27929-9Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer International Publishing Switzerland 2015
The information of publication is updating

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Modelling Non-stationary and Non-separable Spatio-Temporal Changes in Neurodegeneration via Gaussianspatio-temporal modelling of image time series relies on the assumption of stationarity of the local spatial correlation, and on the separability between spatial and temporal processes. These assumptions are often made in order to lead to computationally tractable approaches to longitudinal modellin
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Feature-Space Transformation Improves Supervised Segmentation Across Scannersfeature distribution. However, if training and test images are acquired with different scanners or scanning parameters, their feature distributions can be very different, which can hurt the performance of such techniques..We propose a feature-space-transformation method to overcome these differences
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