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This paper presents a novel method for Bayesian denoising of magnetic resonance (MR) images that bootstraps itself by inferring the prior, i.e., the uncorrupted-image statistics, from the corrupted input data and the knowledge of the Rician noise model. The proposed method relies on principles from empirical Bayes (EB) estimation. It models the prior in a nonparametric Markov random field (MRF) framework...
This paper deals with the handling of uncertainty in the context of cost-effectiveness analyses, through the building of confidence regions for the incremental cost-effectiveness ratio (ICER), to provide a statistical descriptive tool for helping medico-economic decision-making. However, the confidence region for the ICER, mathematically provided, cannot be directly usable for decision-making, because...
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