Machine Learning Meets Medical Imaging - First International Workshop, MLMMI 2015, Held in Conjunction with ICML 2015, Lille, France, July 11, 2015, Revised Selected Papers

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.
 

Sommario

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.

Dettagli sul prodotto

• Con la collaborazione di: Kanwal Bhatia (Editore), Kanwal K. Bhatia (Editore), Herve Lombaert (Editore)
• Editore: Springer, Berlin
• Lingue: Inglese
• Formato: Tascabile
• Pubblicazione: 30.12.2015
• EAN: 9783319279282
• ISBN: 978-3-31-927928-2
• Pagine: 105
• Dimensioni: 156 mm x 4 mm x 235 mm
• Peso: 190 g
• Illustrazioni: X, 105 p. 31 illus. in color.
• Serie: Lecture Notes in Computer Science; Image Processing, Computer Vision, Pattern Recognition, and Graphics
• Categorie: Scienze naturali, medicina, informatica, tecnica > Informatica, EDP > Software applicativo; C, Künstliche Intelligenz, Algorithmen und Datenstrukturen, Algorithms, Artificial Intelligence, Mustererkennung, DV-gestützte Biologie/Bioinformatik, Theoretische Informatik, computer science, bioinformatics, Computer Vision, Theory of Computation, Image Processing and Computer Vision, pattern recognition, Life sciences: general issues, Computers, Algorithms & data structures, Automated Pattern Recognition, Information technology: general issues, Optical data processing, Algorithm Analysis and Problem Complexity, Computational and Systems Biology, Computational Biology/Bioinformatics, Computation by Abstract Devices, Transfer Learning