Research map / Image generation / Image restoration
Metric Learning for Image Registration
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add locally varying multi-Gaussian regularization instead of using global regularization (ablation) | vSVF · CUMC12 | mean target overlap ratio: 0.480 -> 0.520 | Helped |
| add locally varying multi-Gaussian regularization instead of using global regularization (ablation) | vSVF trained on MGH10 · CUMC12 | mean target overlap ratio: 0.480 -> 0.517 | Helped |
| add locally varying multi-Gaussian regularization instead of using global regularization (ablation) | vSVF trained on IBSR18 · CUMC12 | mean target overlap ratio: 0.480 -> 0.518 | Helped |
| train VoxelMorph with additional datasets | VoxelMorph · CUMC12 | mean target overlap ratio: 0.517 -> 0.509 | No clear effect |
| increase the OMT penalty | local metric regularizer · 2D synthetic data | median displacement error: <1 pixel | No clear effect |
Closest papers by meaning
- Deep Learning in Medical Image Registration: Magic or Mirage? (NeurIPS 2024 · similarity 0.88)
- CARL: A Framework for Equivariant Image Registration (CVPR 2025 · similarity 0.88)
- SACB-Net: Spatial-awareness Convolutions for Medical Image Registration (CVPR 2025 · similarity 0.86)
- A Plug-and-Play Image Registration Network (ICLR 2024 · similarity 0.86)
- H-ViT: A Hierarchical Vision Transformer for Deformable Image Registration (CVPR 2024 · similarity 0.85)
- Uncertainty Estimation for Pretrained Medical Image Registration Models via Transformation Equivariance (NeurIPS 2026 · similarity 0.85)
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