AnyTwoReg is a set-based groupwise registration method that achieves zero-shot generalization across variable-length and variable-contrast cardiac MRI sequences by using permutation-invariant feature aggregation.
IEEE transactions on medical imaging38(8), 1788–1800 (2019)
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CT-guided voxel-wise regularization for the displacement field improves whole-body cross-tracer PET registration over global regularization baselines on a 296-patient dataset.
ShapeFuse uses bidirectional cross-modal temporal attention and adaptive gating to fuse deformable shape and texture features for cardiac video classification, outperforming existing fusion strategies on a cine CMR dataset.
Search-MIND delivers a training-free coarse-to-fine optimization pipeline for multi-modal medical image registration using variance-weighted mutual information and broadened structural descriptors that outperforms ANTs and DINO-reg on liver and abdominal datasets.
citing papers explorer
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Set-Based Groupwise Registration for Variable-Length, Variable-Contrast Cardiac MRI
AnyTwoReg is a set-based groupwise registration method that achieves zero-shot generalization across variable-length and variable-contrast cardiac MRI sequences by using permutation-invariant feature aggregation.
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CT-Guided Spatially-varying Regularization for Voxel-Wise Deformable Whole-Body PET Registration
CT-guided voxel-wise regularization for the displacement field improves whole-body cross-tracer PET registration over global regularization baselines on a 296-patient dataset.
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Learning to Unify Deformable Shape and Texture Representations for Cardiac Video Classification
ShapeFuse uses bidirectional cross-modal temporal attention and adaptive gating to fuse deformable shape and texture features for cardiac video classification, outperforming existing fusion strategies on a cine CMR dataset.
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Search-MIND: Training-Free Multi-Modal Medical Image Registration
Search-MIND delivers a training-free coarse-to-fine optimization pipeline for multi-modal medical image registration using variance-weighted mutual information and broadened structural descriptors that outperforms ANTs and DINO-reg on liver and abdominal datasets.