Medical Image Computing And Computer Assisted Intervention

Medical Image Computing and Computer-Assisted Intervention - MICCAI 2014, Part I

Polina Golland, Nobuhiko Hata, Christian Barillot, "Medical Image Computing and Computer-Assisted Intervention - MICCAI 2014, Part I"
English | 2014 | ISBN: 3319104039 | PDF | pages: 868 | 109.9 mb

Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2016  eBooks & eLearning

Posted by roxul at Oct. 26, 2016
Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2016

Sebastien Ourselin and Leo Joskowicz, "Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016"
English | ISBN: 3319467255 | 2016 | 668 pages | PDF | 106 MB

Medical Image Computing and Computer-Assisted Intervention - MICCAI 2013  eBooks & eLearning

Posted by ChrisRedfield at Nov. 9, 2013
Medical Image Computing and Computer-Assisted Intervention - MICCAI 2013

Kensaku Mori, Ichiro Sakuma, Yoshinobu Sato, Christian Barillot (Editor), Nassir Navab - Medical Image Computing and Computer-Assisted Intervention - MICCAI 2013
Published: 2013-09-13 | ISBN: 3642408109, 3642407625, 3642407595 | PDF | 2180 pages | 157 MB

Medical Image Computing and Computer-Assisted Intervention - MICCAI 2007  eBooks & eLearning

Posted by sandhu1 at July 8, 2011
Medical Image Computing and Computer-Assisted Intervention - MICCAI 2007

Medical Image Computing and Computer-Assisted Intervention - MICCAI 2007
Springer; 1 edition | December 10, 2007 | ISBN-10: 3540757562 | 1012 pages | PDF | 39.25 MB

This title is part of a two-volume set that constitute the refereed proceedings of the 10th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2007.

Computational Biomechanics for Medicine: Deformation and Flow (repost)  eBooks & eLearning

Posted by interes at Oct. 17, 2014
Computational Biomechanics for Medicine: Deformation and Flow (repost)

Computational Biomechanics for Medicine: Deformation and Flow by Poul M.F. Nielsen, Adam Wittek and Karol Miller
English | 2012-04-29 | ISBN: 1461431719 | PDF | 110 pages | 3 MB

One of the greatest challenges for mechanical engineers is to extend the success of computational mechanics to fields outside traditional engineering, in particular to biology, biomedical sciences, and medicine. This book is an opportunity for computational biomechanics specialists to present and exchange opinions on the opportunities of applying their techniques to computer-integrated medicine.
Computational Biomechanics for Medicine: Soft Tissues and the Musculoskeletal System [Repost]

Adam Wittek, ‎Poul M.F. Nielsen, ‎Karol Miller - Computational Biomechanics for Medicine: Soft Tissues and the Musculoskeletal System
Published: 2011-06-09 | ISBN: 1441996184 | PDF | 168 pages | 4 MB

Bayesian and grAphical Models for Biomedical Imaging  eBooks & eLearning

Posted by DZ123 at Feb. 20, 2017
Bayesian and grAphical Models for Biomedical Imaging

M. Jorge Cardoso, Ivor Simpson, Tal Arbel, "Bayesian and grAphical Models for Biomedical Imaging"
English | 2014 | ISBN: 3319122886 | PDF | pages: 139 | 14.8 mb

Deep Learning and Data Labeling for Medical Applications  eBooks & eLearning

Posted by AvaxGenius at Feb. 20, 2017
Deep Learning and Data Labeling for Medical Applications

Deep Learning and Data Labeling for Medical Applications By Gustavo Carneiro, Diana Mateus, Loïc Peter, Andrew Bradley, João Manuel R. S. Tavares, Vasileios Belagiannis, João Paulo Papa, Jacinto C. Nascimento, Marco Loog, Zhi Lu, Jaime S. Cardoso, Julien Cornebise
English | PDF | 2016 | 288 Pages | ISBN : 3319469754 | 46.02 MB

This book constitutes the refereed proceedings of two workshops held at the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, in Athens, Greece, in October 2016: the First Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2016, and the Second International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2016. The 28 revised regular papers presented in this book were carefully reviewed and selected from a total of 52 submissions.

Reconstruction, Segmentation, and Analysis of Medical Images  eBooks & eLearning

Posted by hill0 at Jan. 19, 2017
Reconstruction, Segmentation, and Analysis of Medical Images

Reconstruction, Segmentation, and Analysis of Medical Images: First International Workshops, RAMBO 2016 and HVSMR 2016, Held in Conjunction with … Papers (Lecture Notes in Computer Science) by Maria A. Zuluaga
English | 26 Feb. 2017 | ISBN: 3319522795 | 174 Pages | PDF | 39.91 MB

This book constitutes the refereed proceedings of two workshops held at the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, in Athens, Greece, in October 2016: the First International Workshop on Reconstruction and Analysis of Moving Body Organs, RAMBO 2016, and the First International Workshop on Whole-Heart and Great Vessel Segmentation from 3D Cardiovascular MRI in Congenital Heart Disease, HVSMR 2016.
Machine Learning in Medical Imaging: First International Workshop, MLMI 2010, Held in Conjunction with MICCAI 2010, Beijing, Ch

Machine Learning in Medical Imaging: First International Workshop, MLMI 2010, Held in Conjunction with MICCAI 2010, Beijing, China, September 20, 2010, Proceedings
English | September 3, 2010 | ISBN: 3642159478 | 199 Pages | PDF | 5 MB

The first International Workshop on Machine Learning in Medical Imaging, MLMI 2010, was held at the China National Convention Center, Beijing, China on September 20, 2010 in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2010. Machine learning plays an essential role in the medical imaging field, including image segmentation, image registration, computer-aided diagnosis, image fusion, image- guided therapy, image annotation, and image database retrieval. With advances in medical imaging, new imaging modalities, and methodologies such as cone-beam/multi-slice CT, 3D Ultrasound, tomosynthesis, diffusion-weighted MRI, electrical impedance to- graphy, and diffuse optical tomography, new machine-learning algorithms/applications are demanded in the medical imaging field.