Machine Learning Meets Medical Imaging First International Workshop, MLMMI 2015, Held in Conjunction with ICML 2015, Lille, France, July 11, 2015, Revised Selected Papers /
Machine Learning Meets Medical Imaging First International Workshop, MLMMI 2015, Held in Conjunction with ICML 2015, Lille, France, July 11, 2015, Revised Selected Papers / [electronic resource] :
edited by Kanwal Bhatia, Herve Lombaert.
- 1st ed. 2015.
- X, 105 p. 31 illus. in color. online resource.
- Image Processing, Computer Vision, Pattern Recognition, and Graphics, 9487 3004-9954 ; .
- Image Processing, Computer Vision, Pattern Recognition, and Graphics, 9487 .
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.
Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable 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. .
9783319279299
10.1007/978-3-319-27929-9 doi
Computer vision.
Artificial intelligence.
Bioinformatics.
Pattern recognition systems.
Algorithms.
Computer science.
Computer Vision.
Artificial Intelligence.
Computational and Systems Biology.
Automated Pattern Recognition.
Algorithms.
Theory of Computation.
TA1634
006.37
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.
Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable 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. .
9783319279299
10.1007/978-3-319-27929-9 doi
Computer vision.
Artificial intelligence.
Bioinformatics.
Pattern recognition systems.
Algorithms.
Computer science.
Computer Vision.
Artificial Intelligence.
Computational and Systems Biology.
Automated Pattern Recognition.
Algorithms.
Theory of Computation.
TA1634
006.37