Name
Abstract | ||
|---|---|---|
We investigate uncertainty quantification under a sparse Bayesian model of medical image registration. Bayesian modelling has proven powerful to automate the tuning of registration hyperparameters, such as the trade-off between the data and regularization functionals. Sparsity-inducing priors have recently been used to render the parametrization itself adaptive and data-driven. The sparse prior on... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TMI.2016.2623608 | IEEE Transactions on Medical Imaging |
Keywords | Field | DocType |
Uncertainty,Bayes methods,Data models,Markov processes,Adaptation models,Computational modeling,Biomedical imaging | Bayesian inference,Uncertainty quantification,Markov chain Monte Carlo,Bayesian linear regression,Approximate inference,Artificial intelligence,Bayesian statistics,Mathematical optimization,Reversible-jump Markov chain Monte Carlo,Algorithm,Prior probability,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
36 | 2 | 0278-0062 |
Citations | PageRank | References |
4 | 0.45 | 13 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Loïc Le Folgoc | 1 | 51 | 6.48 |
| Hervé Delingette | 2 | 2133 | 207.11 |
| Antonio Criminisi | 3 | 6801 | 394.29 |
| Nicholas Ayache | 4 | 10804 | 1654.36 |