RFR-WWANet：基于加权窗口注意力的恢复特征分辨率网络用于无监督图像配准
RFR-WWANet: Weighted Window Attention-Based Recovery Feature Resolution Network for Unsupervised Image Registration
AI总结：
针对Transformer配准模型丢失细粒度空间信息及窗口注意力不灵活的问题，提出RFR-WWANet，通过恢复特征分辨率和加权窗口注意力实现无监督图像配准，显著提升性能。
摘要：
Swin Transformer近年来因其计算效率和长程建模能力而在医学图像分析领域受到关注。凭借这些特性，Swin Transformer适用于在复杂的腹部图像配准任务中建立不同位置对应体素之间更远距离的关系。然而，基于Transformer的配准模型将多个体素合并为单个语义令牌。这种合并过程限制了Transformer建模和生成粗粒度空间信息的能力。为解决这一问题，我们提出了恢复特征分辨率网络（RFRNet），该网络使Transformer能够为更高分辨率级别贡献细粒度空间信息和丰富的语义对应关系。此外，移位窗口分区操作不够灵活，意味着它们无法感知不确定距离上的语义信息，也无法自动建立窗口之间的全局连接。因此，我们提出了一种加权窗口注意力（WWA）机制，以自动构建窗口间的全局交互。该机制在Swin Transformer块内的常规和循环移位窗口分区操作之后实现。所提出的无监督可变形图像配准模型，名为RFR-WWANet，能够检测长程相关性，并促进解剖结构的有意义的语义关联。定性和定量结果表明，RFR-WWANet相较于当前最先进的方法取得了显著改进。消融实验证明了RFRNet和WWA设计的有效性。我们的代码可在https://github.com/MingR-Ma/RFR-WWANet获取。
英文摘要：
The Swin transformer has recently attracted attention in medical image analysis due to its computational efficiency and long-range modeling capability. Owing to these properties, the Swin Transformer is suitable for establishing more distant relationships between corresponding voxels in different positions in complex abdominal image registration tasks. However, the registration models based on transformers combine multiple voxels into a single semantic token. This merging process limits the transformers to model and generate coarse-grained spatial information. To address this issue, we propose Recovery Feature Resolution Network (RFRNet), which allows the transformer to contribute fine-grained spatial information and rich semantic correspondences to higher resolution levels. Furthermore, shifted window partitioning operations are inflexible, indicating that they cannot perceive the semantic information over uncertain distances and automatically bridge the global connections between windows. Therefore, we present a Weighted Window Attention (WWA) to build global interactions between windows automatically. It is implemented after the regular and cyclic shift window partitioning operations within the Swin transformer block. The proposed unsupervised deformable image registration model, named RFR-WWANet, detects the long-range correlations, and facilitates meaningful semantic relevance of anatomical structures. Qualitative and quantitative results show that RFR-WWANet achieves significant improvements over the current state-of-the-art methods. Ablation experiments demonstrate the effectiveness of the RFRNet and WWA designs. Our code is available at \url{https://github.com/MingR-Ma/RFR-WWANet}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
