标题： U-RWKV：具有方向自适应RWKV的轻量级医学图像分割 显示英文标题

标题： U-RWKV: Lightweight medical image segmentation with direction-adaptive RWKV

摘要： 实现医疗保健可及性的公平性需要轻量级且高性能的解决方案用于医学图像分割，特别是在资源有限的环境中。 现有的方法如U-Net及其变体通常受到有限的全局有效感受野（ERFs）的困扰，这阻碍了它们捕捉长距离依赖关系的能力。 为了解决这个问题，我们提出了U-RWKV，这是一种利用循环加权键值（RWKV）架构的新框架，能够在O(N)计算成本下实现高效的长距离建模。 该框架引入了两个关键创新：方向自适应RWKV模块（DARM）和阶段自适应压缩与激励模块（SASE）。 DARM采用双-RWKV和四扫描机制来跨图像聚合上下文线索，在减轻方向偏差的同时保留全局上下文并保持高计算效率。 SASE动态适应不同的特征提取阶段，平衡高分辨率细节的保留和语义关系的捕捉。 实验表明，U-RWKV在计算效率高的情况下实现了最先进的分割性能，为在资源受限环境中普及先进的医学成像技术提供了实用的解决方案。 代码可在https://github.com/hbyecoding/U-RWKV获取。 显示英文摘要

摘要： Achieving equity in healthcare accessibility requires lightweight yet high-performance solutions for medical image segmentation, particularly in resource-limited settings. Existing methods like U-Net and its variants often suffer from limited global Effective Receptive Fields (ERFs), hindering their ability to capture long-range dependencies. To address this, we propose U-RWKV, a novel framework leveraging the Recurrent Weighted Key-Value(RWKV) architecture, which achieves efficient long-range modeling at O(N) computational cost. The framework introduces two key innovations: the Direction-Adaptive RWKV Module(DARM) and the Stage-Adaptive Squeeze-and-Excitation Module(SASE). DARM employs Dual-RWKV and QuadScan mechanisms to aggregate contextual cues across images, mitigating directional bias while preserving global context and maintaining high computational efficiency. SASE dynamically adapts its architecture to different feature extraction stages, balancing high-resolution detail preservation and semantic relationship capture. Experiments demonstrate that U-RWKV achieves state-of-the-art segmentation performance with high computational efficiency, offering a practical solution for democratizing advanced medical imaging technologies in resource-constrained environments. The code is available at https://github.com/hbyecoding/U-RWKV.