Machine Learning Meets Medical Imaging

First International Workshop, MLMMI 2015, Held in Conjunction with ICML 2015, Lille, France, July 11, 2015, Revised Selected Papers
Herausgegeben:Bhatia, Kanwal K.; Lombaert, Herve

• Broschiertes Buch

Produktbeschreibung

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/_ 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 the medical imaging community to current trends in machinelearning.

Produktdetails

• Lecture Notes in Computer Science 9487
• Verlag: Springer / Springer International Publishing / Springer, Berlin
• Artikelnr. des Verlages: 978-3-319-27928-2
• 1st ed. 2015
• Seitenzahl: 116
• Erscheinungstermin: 30. Dezember 2015
• Englisch
• Abmessung: 235mm x 155mm x 7mm
• Gewicht: 190g
• ISBN-13: 9783319279282
• ISBN-10: 3319279289
• Artikelnr.: 44072602

Inhaltsangabe

Retrospectivemotion correction of magnitude-input MR images.- Automatic Brain Localizationin Fetal MRI Using Superpixel Graphs.- Learning Deep Temporal Representationsfor fMRI Brain Decoding.- Modelling Non-Stationary and Non-SeparableSpatio-Temporal Changes in Neurodegeneration via Gaussian Process Convolution.-Improving MRI brain image classification with anatomical regional kernels.- AGraph Based Classification Method for Multiple Sclerosis Clinical Form UsingSupport Vector Machine.- Classification of Alzheimer's Disease usingDiscriminant Manifolds of Hippocampus Shapes.- Transfer Learning for ProstateCancer Mapping Based on Multicentric MR imaging databases.