Machine Learning Meets Medical Imaging

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.

Includes supplementary material: sn.pub/extras

Inhalt

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.