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Bayesian and grAphical Models for Biomedical Imaging : First International Workshop, BAMBI 2014, Cambridge, MA, USA, September 18, 2014, Revised Selected Papers / edited by M. Jorge Cardoso, Ivor Simpson, Tal Arbel, Doina Precup, Annemie Ribbens.
- Format:
- Book
- Series:
- Computer Science (SpringerNature-11645)
- LNCS sublibrary. Theoretical computer science and general issues 2512-2029 ; SL 1, 8677
- Theoretical Computer Science and General Issues, 2512-2029 ; 8677
- Language:
- English
- Subjects (All):
- Algorithms.
- Artificial intelligence.
- Computer vision.
- Pattern recognition systems.
- Computer graphics.
- Computer science-Mathematics.
- Discrete mathematics.
- Artificial Intelligence.
- Computer Vision.
- Automated Pattern Recognition.
- Computer Graphics.
- Discrete Mathematics in Computer Science.
- Local Subjects:
- Algorithms.
- Artificial Intelligence.
- Computer Vision.
- Automated Pattern Recognition.
- Computer Graphics.
- Discrete Mathematics in Computer Science.
- Physical Description:
- 1 online resource (X, 131 pages) : 54 illustrations
- Edition:
- 1st ed. 2014.
- Contained In:
- Springer Nature eBook
- Place of Publication:
- Cham : Springer International Publishing : Imprint: Springer, 2014.
- System Details:
- text file PDF
- Summary:
- 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.
- Other Format:
- Printed edition:
- ISBN:
- 978-3-319-12289-2
- 9783319122892
- Access Restriction:
- Restricted for use by site license.
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