Overview
Part of the book series: Lecture Notes in Computer Science (LNCS, volume 8677)
Part of the book sub series: Theoretical Computer Science and General Issues (LNTCS)
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About this book
This book constitutes the refereed proceedings of the First International Workshop on Bayesian and grAphical Models for Biomedical Imaging, BAMBI 2014, held in Cambridge, MA, USA, in September 2014 as a satellite event of the 17th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2014.
The 11 revised full papers presented were carefully reviewed and selected from numerous submissions with a key aspect on probabilistic modeling applied to medical image analysis. The objectives of this workshop compared to other workshops, e.g. machine learning in medical imaging, have a stronger mathematical focus on the foundations of probabilistic modeling and inference. The papers highlight the potential of using Bayesian or random field graphical models for advancing scientific research in biomedical image analysis or for the advancement of modeling and analysis of medical imaging data.
The 11 revised full papers presented were carefully reviewed and selected from numerous submissions with a key aspect on probabilistic modeling applied to medical image analysis. The objectives of this workshop compared to other workshops, e.g. machine learning in medical imaging, have a stronger mathematical focus on the foundations of probabilistic modeling and inference. The papers highlight the potential of using Bayesian or random field graphical models for advancing scientific research in biomedical image analysis or for the advancement of modeling and analysis of medical imaging data.
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Keywords
- bayesian modeling
- biomedical images
- classification
- computer vision
- functional modeling
- graphical modeling
- image analysis
- image segmentation
- inference algorithms
- machine learning
- multi-modal modeling
- neuro imaging
- probabilistic models
- reconstruction
- registration
- segmentation
- structural modeling
- algorithm analysis and problem complexity
Table of contents (11 papers)
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Front Matter
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Back Matter
Editors and Affiliations
Bibliographic Information
Book Title: Bayesian and grAphical Models for Biomedical Imaging
Book Subtitle: First International Workshop, BAMBI 2014, Cambridge, MA, USA, September 18, 2014, Revised Selected Papers
Editors: M. Jorge Cardoso, Ivor Simpson, Tal Arbel, Doina Precup, Annemie Ribbens
Series Title: Lecture Notes in Computer Science
DOI: https://doi.org/10.1007/978-3-319-12289-2
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer International Publishing Switzerland 2014
Softcover ISBN: 978-3-319-12288-5Published: 02 October 2014
eBook ISBN: 978-3-319-12289-2Published: 22 September 2014
Series ISSN: 0302-9743
Series E-ISSN: 1611-3349
Edition Number: 1
Number of Pages: X, 131
Number of Illustrations: 54 b/w illustrations
Topics: Algorithm Analysis and Problem Complexity, Artificial Intelligence, Image Processing and Computer Vision, Pattern Recognition, Computer Graphics, Discrete Mathematics in Computer Science