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Cardoso M.J., Simpson I., Arbel T., Precup D., Ribbens A. (eds.) Bayesian and grAphical Models for Biomedical Imaging

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Cardoso M.J., Simpson I., Arbel T., Precup D., Ribbens A. (eds.) Bayesian and grAphical Models for Biomedical Imaging
New York: Springer, 2014. — 139 p.
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.
N3 Bias Field Correction Explained as a Bayesian Modeling Method
A Bayesian Approach to Distinguishing Interdigitated Muscles in the Tongue from Limited Diffusion Weighted Imaging
Optimal Joint Segmentation and Tracking of Escherichia Coli in the Mother Machine
Physiologically Informed Bayesian Analysis of ASL fMRI Data
Bone Reposition Planning for Corrective Surgery Using Statistical Shape Model: Assessment of Differential Geometrical Features
An Inference Language for Imaging
An MRF-Based Discrete Optimization Framework for Combined DCE-MRI Motion Correction and Pharmacokinetic Parameter Estimation
Learning Imaging Biomarker Trajectories from Noisy Alzheimer’s Disease Data Using a Bayesian Multilevel Model
Four Neuroimaging Questions that P-Values Cannot Answer (and Bayesian Analysis Can)
Spherical Topic Models for Imaging Phenotype Discovery in Genetic Studies
A Generative Model for Automatic Detection of Resolving Multiple Sclerosis Lesions
Back Matter
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