Normal 0 false false false EN-US X-NONE X-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 the First International Workshop on Machine Learning in Medical Imaging, MLMMI 2015, held in July 2015 in Lille, France, in conjunction with the 32nd International Conference on Machine Learning, ICML 2015. The 10 papers presented in this volume were carefully reviewed and selected for inclusion in the book. The papers communicate the specific needs and nuances of medical imaging to the machine learning community while exposing the medical imaging community to current trends in machine learning.  
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The papers communicate thespecific needs and nuances of medical imaging to the machine learning communitywhile exposing the medical imaging community to current trends in machinelearning.
Retrospective motion correction of magnitude-input MR images.- Automatic Brain Localization in Fetal MRI Using Superpixel Graphs.- Learning Deep Temporal Representations for fMRI Brain Decoding.- Modelling Non-Stationary and Non-Separable Spatio-Temporal Changes in Neurodegeneration via Gaussian Process Convolution.- Improving MRI brain image classification with anatomical regional kernels.- A Graph Based Classification Method for Multiple Sclerosis Clinical Form Using Support Vector Machine.- Classification of Alzheimer’s Disease using Discriminant Manifolds of Hippocampus Shapes.- Transfer Learning for Prostate Cancer Mapping Based on Multicentric MR imaging databases.
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Includes supplementary material: sn.pub/extras

Produktdetaljer

ISBN
9783319279282
Publisert
2015-12-30
Utgiver
Vendor
Springer International Publishing AG
Høyde
235 mm
Bredde
155 mm
Aldersnivå
Research, UP, P, 05, 06
Språk
Product language
Engelsk
Format
Product format
Heftet