Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging Miccai 2016 International Workshops, McV and Bambi, Athens, Greece, October 21, 2016, Revised Selected Papers |
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| Editor:
| Müller, Henning
Kelm, B. Michael
Arbel, Tal
Cai, Weidong
Cardoso, M. Jorge
Langs, Georg
Menze, Bjoern
Metaxas, Dimitris
Montillo, Albert
Wells III, William M.
Zhang, Shaoting
Chung, Albert C. S.
Jenkinson, Mark
Ribbens, Annemie |
| Series title: | Lecture Notes in Computer Science Ser. |
| ISBN: | 978-3-319-61187-7 |
| Publication Date: | Jul 2017 |
| Publisher: | Springer International Publishing AG
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| Imprint: | Springer |
| Book Format: | Paperback |
| List Price: | USD $69.99USD $69.99USD $54.99 |
Book Description:
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This book constitutes the thoroughly refereed post-workshop proceedings of the International Workshop on Medical Computer Vision, MCV 2016, and of the International Workshop on Bayesian and grAphical Models for Biomedical Imaging, BAMBI 2016, held in Athens, Greece, in October 2016, held in conjunction with the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016. The 13 papers presented in MCV workshop and the 6 papers...
More Description
This book constitutes the thoroughly refereed post-workshop proceedings of the International Workshop on Medical Computer Vision, MCV 2016, and of the International Workshop on Bayesian and grAphical Models for Biomedical Imaging, BAMBI 2016, held in Athens, Greece, in October 2016, held in conjunction with the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016.
The 13 papers presented in MCV workshop and the 6 papers presented in BAMBI workshop were carefully reviewed and selected from numerous submissions.
The goal of the MCV workshop is to explore the use of "big data" algorithms for harvesting, organizing and learning from large-scale medical imaging data sets and for general-purpose automatic understanding of medical images.
The BAMBI workshop aims to highlight the potential of using Bayesian or random field graphical models for advancing research in biomedical image analysis.