# Correlation-aware Coarse-to-fine MLPs for Deformable Medical Image Registration

arXiv: https://arxiv.org/abs/2406.00123 · HTML: https://mlsanity.com/paper/2406.00123/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 60 references that are in the map.
- First posted on arXiv 31 May 2024.

## Builds on (most cited first, 30 of 36)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [MLP-Mixer: An all-MLP Architecture for Vision](https://mlsanity.com/paper/2105.01601/) (2021)
- [Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2105.05537/) (2021)
- [VoxelMorph: A Learning Framework for Deformable Medical Image Registration](https://mlsanity.com/paper/1809.05231/) (2018)
- [Pay Attention to MLPs](https://mlsanity.com/paper/2105.08050/) (2021)
- [MAXIM: Multi-Axis MLP for Image Processing](https://mlsanity.com/paper/2201.02973/) (2022)
- [Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces](https://mlsanity.com/paper/1903.03545/) (2019)
- [Large Deformation Diffeomorphic Image Registration with Laplacian Pyramid Networks](https://mlsanity.com/paper/2006.16148/) (2020)
- [Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning](https://mlsanity.com/paper/2112.04489/) (2021)
- [Deep Learning in Medical Image Registration: A Survey](https://mlsanity.com/paper/1903.02026/) (2019)
- [Fast Symmetric Diffeomorphic Image Registration with Convolutional Neural Networks](https://mlsanity.com/paper/2003.09514/) (2020)
- [Recursive Cascaded Networks for Unsupervised Medical Image Registration](https://mlsanity.com/paper/1907.12353/) (2019)
- [S2-MLP: Spatial-Shift MLP Architecture for Vision](https://mlsanity.com/paper/2106.07477/) (2021)
- [Hire-MLP: Vision MLP via Hierarchical Rearrangement](https://mlsanity.com/paper/2108.13341/) (2021)
- [Are we ready for a new paradigm shift? A Survey on Visual Deep MLP](https://mlsanity.com/paper/2111.04060/) (2021)
- [Learning a Probabilistic Model for Diffeomorphic Registration](https://mlsanity.com/paper/1812.07460/) (2018)
- [DiffuseMorph: Unsupervised Deformable Image Registration Using Diffusion Model](https://mlsanity.com/paper/2112.05149/) (2021)
- [Dual-Stream Pyramid Registration Network](https://mlsanity.com/paper/1909.11966/) (2019)
- [U-Net vs Transformer: Is U-Net Outdated in Medical Image Registration?](https://mlsanity.com/paper/2208.04939/) (2022)
- [Sparse MLP for Image Recognition: Is Self-Attention Really Necessary?](https://mlsanity.com/paper/2109.05422/) (2021)
- [DeepFLASH: An Efficient Network for Learning-based Medical Image Registration](https://mlsanity.com/paper/2004.02097/) (2020)
- [The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients](https://mlsanity.com/paper/2112.06979/) (2021)
- [Non-iterative Coarse-to-fine Transformer Networks for Joint Affine and Deformable Image Registration](https://mlsanity.com/paper/2307.03421/) (2023)
- [Non-iterative Coarse-to-fine Registration based on Single-pass Deep Cumulative Learning](https://mlsanity.com/paper/2206.12596/) (2022)
- [Biomedical image analysis competitions: The state of current participation practice](https://mlsanity.com/paper/2212.08568/) (2022)
- [FAIM -- A ConvNet Method for Unsupervised 3D Medical Image Registration](https://mlsanity.com/paper/1811.09243/) (2018)
- [Enhancing Medical Image Registration via Appearance Adjustment Networks](https://mlsanity.com/paper/2103.05213/) (2021)

## Built on by (most cited first, 30 of 38)

- [MatchAnything: Universal Cross-Modality Image Matching with Large-Scale Pre-Training](https://mlsanity.com/paper/2501.07556/) (2025)
- [Mamba? Catch The Hype Or Rethink What Really Helps for Image Registration](https://mlsanity.com/paper/2407.19274/) (2024)
- [DiffKillR: Killing and Recreating Diffeomorphisms for Cell Annotation in Dense Microscopy Images](https://mlsanity.com/paper/2410.03058/) (2024)
- [Bidirectional Recurrence for Cardiac Motion Tracking with Gaussian Process Latent Coding](https://mlsanity.com/paper/2410.20752/) (2024)
- [Unsupervised Deformable Image Registration with Structural Nonparametric Smoothing](https://mlsanity.com/paper/2506.10813/) (2025)
- [Encoder-Only Image Registration](https://mlsanity.com/paper/2509.00451/) (2025)
- [Gaussian Primitives for Deformable Image Registration](https://mlsanity.com/paper/2406.03394/) (2024)
- [Unsupervised learning of spatially varying regularization for diffeomorphic image registration](https://mlsanity.com/paper/2412.17982/) (2024)
- [UniReg: Conditional Unified Model for Medical Image Registration](https://mlsanity.com/paper/2503.12868/) (2025)
- [SACB-Net: Spatial-awareness Convolutions for Medical Image Registration](https://mlsanity.com/paper/2503.19592/) (2025)
- [VoxelOpt: Voxel-Adaptive Message Passing for Discrete Optimization in Deformable Abdominal CT Registration](https://mlsanity.com/paper/2506.19975/) (2025)
- [Auto-regressive transformation for image alignment](https://mlsanity.com/paper/2505.04864/) (2025)
- [Implicit Deformable Medical Image Registration with Learnable Kernels](https://mlsanity.com/paper/2506.02150/) (2025)
- [Mono-Modalizing Extremely Heterogeneous Multi-Modal Medical Image Registration](https://mlsanity.com/paper/2506.15596/) (2025)
- [UniFuse: A Unified All-in-One Framework for Multi-Modal Medical Image Fusion Under Diverse Degradations and Misalignments](https://mlsanity.com/paper/2506.22736/) (2025)
- [Dyna3DGR: 4D Cardiac Motion Tracking with Dynamic 3D Gaussian Representation](https://mlsanity.com/paper/2507.16608/) (2025)
- [T-DuMpRa: Teacher-guided Dual-path Multi-prototype Retrieval Augmented framework for fine-grained medical image classification](https://mlsanity.com/paper/2604.17360/) (2026)
- [LayersReg: A Layer-by-Layer Progressive Regressor for Reliable Intraoperative 3D/2D Registration](https://mlsanity.com/paper/2606.26647/) (2026)
- [Efficient Brain Imaging Analysis for Alzheimer's and Dementia Detection Using Convolution-Derivative Operations](https://mlsanity.com/paper/2411.13490/) (2024)
- [Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain](https://mlsanity.com/paper/2411.16123/) (2024)
- [FetDTIAlign: A Deep Learning Framework for Affine and Deformable Registration of Fetal Brain dMRI](https://mlsanity.com/paper/2502.01057/) (2025)
- [NCF: Neural Correspondence Field for Medical Image Registration](https://mlsanity.com/paper/2503.00760/) (2025)
- [Deformable Medical Image Registration with Effective Anatomical Structure Representation and Divide-and-Conquer Network](https://mlsanity.com/paper/2506.19222/) (2025)
- [A Multimodal Cross-View Model for Predicting Postoperative Neck Pain in Cervical Spondylosis Patients](https://mlsanity.com/paper/2509.02256/) (2025)
- [Toward Next-generation Medical Vision Backbones: Modeling Finer-grained Long-range Visual Dependency](https://mlsanity.com/paper/2509.11328/) (2025)
- [Deformable Medical Image Registration with KAN-based Implicit Neural Representations](https://mlsanity.com/paper/2509.22874/) (2025)
- [Efficient Large-Deformation Medical Image Registration via Recurrent Dynamic Correlation](https://mlsanity.com/paper/2510.22380/) (2025)
- [MorphSeek: Fine-grained Latent Representation-Level Policy Optimization for Deformable Image Registration](https://mlsanity.com/paper/2511.17392/) (2025)
- [Unsupervised Deformable Image Registration with Local-Global Attention and Image Decomposition](https://mlsanity.com/paper/2601.14337/) (2026)
- [FMIR, a foundation model-based Image Registration Framework for Robust Image Registration](https://mlsanity.com/paper/2601.17529/) (2026)

## Abstract (arXiv)

Deformable image registration is a fundamental step for medical image analysis. Recently, transformers have been used for registration and outperformed Convolutional Neural Networks (CNNs). Transformers can capture long-range dependence among image features, which have been shown beneficial for registration. However, due to the high computation/memory loads of self-attention, transformers are typically used at downsampled feature resolutions and cannot capture fine-grained long-range dependence at the full image resolution. This limits deformable registration as it necessitates precise dense correspondence between each image pixel. Multi-layer Perceptrons (MLPs) without self-attention are efficient in computation/memory usage, enabling the feasibility of capturing fine-grained long-range dependence at full resolution. Nevertheless, MLPs have not been extensively explored for image registration and are lacking the consideration of inductive bias crucial for medical registration tasks. In this study, we propose the first correlation-aware MLP-based registration network (CorrMLP) for deformable medical image registration. Our CorrMLP introduces a correlation-aware multi-window MLP block in a novel coarse-to-fine registration architecture, which captures fine-grained multi-range dependence to perform correlation-aware coarse-to-fine registration. Extensive experiments with seven public medical datasets show that our CorrMLP outperforms state-of-the-art deformable registration methods.