Kanwal Bhatia (EDT), Herve Lombaert (EDT) Machine Learning Meets Medical Imaging

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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 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.