The advent of deep-learning-based registration networks has addressed the
time-consuming challenge in traditional iterative methods.However, the
potential of current registration networks for comprehensively capturing
spatial relationships has not been fully explored, leading to inadequate
performance in large-deformation image registration.The pure convolutional
neural networks (CNNs) neglect feature enhancement, while current
Transformer-based networks are susceptible to information redundancy.To
alleviate these issues, we propose a pyramid attention network (PAN) for
deformable medical image registration.Specifically, the proposed PAN
incorporates a dual-stream pyramid encoder with channel-wise attention to boost
the feature representation.Moreover, a multi-head local attention Transformer
is introduced as decoder to analyze motion patterns and generate deformation
fields.Extensive experiments on two public brain magnetic resonance imaging
(MRI) datasets and one abdominal MRI dataset demonstrate that our method
achieves favorable registration performance, while outperforming several
CNN-based and Transformer-based registration networks.Our code is publicly
available at this https URL

我们提出了金字塔注意力网络（PAN）用于可变形医学图像配准，通过引入双流金字塔编码器和局部多头注意力变换器实现特征表示和分析运动模式，取得了较好的配准性能。

金字塔注意力网络用于医学图像配准

Pyramid Attention Network for Medical Image Registration

Convolutional neural networks (CNNs) have been widely used to build deep
learning models for medical image registration, but manually designed network
architectures are not necessarily optimal. This paper presents a hierarchical
NAS framework (HNAS-Reg), consisting of both convolutional operation search and
network topology search, to identify the optimal network architecture for
deformable medical image registration. To mitigate the computational overhead
and memory constraints, a partial channel strategy is utilized without losing
optimization quality. Experiments on three datasets, consisting of 636
T1-weighted magnetic resonance images (MRIs), have demonstrated that the
proposal method can build a deep learning model with improved image
registration accuracy and reduced model size, compared with state-of-the-art
image registration approaches, including one representative traditional
approach and two unsupervised learning-based approaches.

这篇论文提出了一个层次化的 NAS 框架（HNAS-Reg），通过卷积操作搜索和网络拓扑搜索，寻找最优网络结构用于可变形医学图像配准，实验证实该方法相比传统方法和两种无监督学习方法，在配准准确性和模型大小方面有明显的改进。

HNAS-reg：用于可变形医学图像配准的分层神经架构搜索

HNAS-reg: hierarchical neural architecture search for deformable medical  image registration

An effective backbone network is important to deep learning-based Deformable
Medical Image Registration (DMIR), because it extracts and matches the features
between two images to discover the mutual correspondence for fine registration.
However, the existing deep networks focus on single image situation and are
limited in registration task which is performed on paired images. Therefore, we
advance a novel backbone network, XMorpher, for the effective corresponding
feature representation in DMIR. 1) It proposes a novel full transformer
architecture including dual parallel feature extraction networks which exchange
information through cross attention, thus discovering multi-level semantic
correspondence while extracting respective features gradually for final
effective registration. 2) It advances the Cross Attention Transformer (CAT)
blocks to establish the attention mechanism between images which is able to
find the correspondence automatically and prompts the features to fuse
efficiently in the network. 3) It constrains the attention computation between
base windows and searching windows with different sizes, and thus focuses on
the local transformation of deformable registration and enhances the computing
efficiency at the same time. Without any bells and whistles, our XMorpher gives
Voxelmorph 2.8% improvement on DSC , demonstrating its effective representation
of the features from the paired images in DMIR. We believe that our XMorpher
has great application potential in more paired medical images. Our XMorpher is
open on this https URL

提出了一种名为 XMorpher 的转换网络用于医学图像变形注册任务，在该网络中，引入了全新的 Transformer 模块和注意力机制，能够逐渐提取各自的特征和发现语义对应关系，从而实现有效的变形注册。