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245 1 0 _aMachine Learning Meets Medical Imaging :
_bFirst International Workshop, MLMMI 2015, Held in Conjunction with ICML 2015, Lille, France, July 11, 2015, Revised Selected Papers /
_cedited by Kanwal Bhatia, Herve Lombaert.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2015.
300 _a1 online resource
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
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347 _atext file
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490 0 _aImage Processing, Computer Vision, Pattern Recognition, and Graphics ;
_v9487
505 0 _aRetrospective 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.
520 _aNormal 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. .
650 0 _aComputer vision.
650 0 _aArtificial intelligence.
650 0 _aBioinformatics.
650 0 _aOptical pattern recognition.
650 0 _aComputer software.
650 0 _aComputer science.
650 1 4 _aImage Processing and Computer Vision.
_0http://scigraph.springernature.com/things/product-market-codes/I22021
650 2 4 _aArtificial Intelligence.
_0http://scigraph.springernature.com/things/product-market-codes/I21000
650 2 4 _aComputational Biology/Bioinformatics.
_0http://scigraph.springernature.com/things/product-market-codes/I23050
650 2 4 _aPattern Recognition.
_0http://scigraph.springernature.com/things/product-market-codes/I2203X
650 2 4 _aAlgorithm Analysis and Problem Complexity.
_0http://scigraph.springernature.com/things/product-market-codes/I16021
650 2 4 _aComputation by Abstract Devices.
_0http://scigraph.springernature.com/things/product-market-codes/I16013
700 1 _aBhatia, Kanwal.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aLombaert, Herve.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
856 4 0 _uhttps://doi.org/10.1007/978-3-319-27929-9
_3Springer eBooks
_zOnline access link to the resource
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