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E-grāmata: Bayesian and grAphical Models for Biomedical Imaging: First International Workshop, BAMBI 2014, Cambridge, MA, USA, September 18, 2014, Revised Selected Papers

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Bayesian and grAphical Models for Biomedical Imaging: First International Workshop, BAMBI 2014, Cambridge, MA, USA, September 18, 2014, Revised Selected PapersPalielināt
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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.
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 Alzheimers 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. 
Biežāk uzdotie jautājumi par e-grāmatām Biežāk uzdotie jautājumi par e-grāmatām
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