Machine Learning Meets Medical Imaging: Lecture Notes in Computer Science, cartea 9487
Editat de Kanwal Bhatia, Herve Lombaerten Limba Engleză Paperback – 30 dec 2015
/* 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.
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Specificații
ISBN-13: 9783319279282
ISBN-10: 3319279289
Pagini: 116
Ilustrații: X, 105 p. 31 illus. in color.
Dimensiuni: 155 x 235 x 7 mm
Greutate: 0.19 kg
Ediția:1st edition 2015
Editura: Springer
Colecția Lecture Notes in Computer Science
Seria Lecture Notes in Computer Science
Locul publicării:Cham, Switzerland
ISBN-10: 3319279289
Pagini: 116
Ilustrații: X, 105 p. 31 illus. in color.
Dimensiuni: 155 x 235 x 7 mm
Greutate: 0.19 kg
Ediția:1st edition 2015
Editura: Springer
Colecția Lecture Notes in Computer Science
Seria Lecture Notes in Computer Science
Locul publicării:Cham, Switzerland
Public țintă
ResearchCuprins
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.