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Found 8 papers, 3 papers with code
NPB-REC: A Non-parametric Bayesian Deep-learning Approach for Undersampled MRI Reconstruction with Uncertainty Estimation
We introduce "NPB-REC", a non-parametric fully Bayesian framework, for MRI reconstruction from undersampled data with uncertainty estimation.
A self-attention model for robust rigid slice-to-volume registration of functional MRI
Furthermore, our approach exhibits significantly faster registration speed compared to conventional iterative methods ($0. 096$ sec.
NPB-REC: Non-parametric Assessment of Uncertainty in Deep-learning-based MRI Reconstruction from Undersampled Data
We demonstrated the added-value of our approach on the multi-coil brain MRI dataset, from the fastmri challenge, in comparison to the baseline E2E-VarNet with and without inference-time dropout.
Deep-Learning based Motion Correction for Myocardial T1 Mapping
In the presence of motion, the different T1-weighted images are not aligned.
NPBDREG: Uncertainty Assessment in Diffeomorphic Brain MRI Registration using a Non-parametric Bayesian Deep-Learning Based Approach
The NPBDREG shows a better correlation of the predicted uncertainty with out-of-distribution data ($r>0. 95$ vs. $r<0. 5$) as well as a 7. 3%improvement in the registration accuracy (Dice score, $0. 74$ vs. $0. 69$, $p \ll 0. 01$), and 18% improvement in registration smoothness (percentage of folds in the deformation field, 0. 014 vs. 0. 017, $p \ll 0. 01$).
Unsupervised Deep-Learning Based Deformable Image Registration: A Bayesian Framework
We demonstrated the added-value of our Basyesian unsupervised DL-based registration framework on the MNIST and brain MRI (MGH10) datasets in comparison to the VoxelMorph unsupervised DL-based image registration framework.
Texture and Structure Two-view Classification of Images
Textural and structural features can be regraded as "two-view" feature sets.
On the Self-Similarity of Natural Stochastic Textures
We firstly decompose a textured image into two layers corresponding to its texture and structure, and show that the layer representing the stochastic texture is characterized by random phase of uniform distribution, unlike the phase of the structured information which is coherent.
