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Deformable Medical Image Registration
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논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Implicit Deformable Medical Image Registration with Learnable Kernels
FF-PNet: A Pyramid Network Based on Feature and Field for Brain Image Registration
IMPACT: A Generic Semantic Loss for Multimodal Medical Image Registration
Advancing Deformable Medical Image Registration with Multi-axis Cross-covariance Attention
NestedMorph: Enhancing Deformable Medical Image Registration with Nested Attention Mechanisms
Dual-Attention Frequency Fusion at Multi-Scale for Joint Segmentation and Deformable Medical Image Registration
H-SGANet: Hybrid Sparse Graph Attention Network for Deformable Medical Image Registration
Correlation-aware Coarse-to-fine MLPs for Deformable Medical Image Registration
A light-weight rectangular decomposition large kernel convolution network for deformable medical image registration.
Residual Aligner-based Network (RAN): Motion-separable structure for coarse-to-fine discontinuous deformable registration
AutoFuse: Automatic Fusion Networks for Deformable Medical Image Registration
HNAS-reg: hierarchical neural architecture search for deformable medical image registration
SITReg: Multi-resolution architecture for symmetric, inverse consistent, and topology preserving image registration
Learning Homeomorphic Image Registration via Conformal-Invariant Hyperelastic Regularisation
ABN: Anti-Blur Neural Networks for Multi-Stage Deformable Image Registration
XMorpher: Full Transformer for Deformable Medical Image Registration via Cross Attention
Automated Learning for Deformable Medical Image Registration by Jointly Optimizing Network Architectures and Objective Functions
Coordinate Translator for Learning Deformable Medical Image Registration
A Transformer-based Network for Deformable Medical Image Registration
Stochastic Planner-Actor-Critic for Unsupervised Deformable Image Registration
Fast 3D registration with accurate optimisation and little learning for Learn2Reg 2021
Unsupervised Deformable Medical Image Registration via Pyramidal Residual Deformation Fields Estimation
Few Labeled Atlases are Necessary for Deep-Learning-Based Segmentation
Learning Conditional Deformable Templates with Convolutional Networks
Deformable Medical Image Registration Using a Randomly-Initialized CNN as Regularization Prior
Deformable Registration Using Average Geometric Transformations for Brain MR Images
One Shot Learning for Deformable Medical Image Registration and Periodic Motion Tracking
Metric Learning for Image Registration
Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces
Learning a Probabilistic Model for Diffeomorphic Registration
VoxelMorph: A Learning Framework for Deformable Medical Image Registration
An Unsupervised Learning Model for Deformable Medical Image Registration
Unsupervised End-to-end Learning for Deformable Medical Image Registration
Deformable medical image registration: setting the state of the art with discrete methods
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.