Bayesian and grAphical Models for Biomedical Imaging, 2014 First International Workshop, BAMBI 2014, Cambridge, MA, USA, September 18, 2014, Revised Selected Papers Theoretical Computer Science and General Issues Series
Coordonnateurs : Cardoso M. Jorge, Simpson Ivor, Arbel Tal, Precup Doina, Ribbens Annemie
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.
Date de parution : 10-2014
Ouvrage de 131 p.
15.5x23.5 cm
Thèmes de Bayesian and grAphical Models for Biomedical Imaging :
Mots-clés :
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
