Medical image segmentation is a critical task that plays a vital role in diagnosis, treatment planning, and disease monitoring. Accurate segmentation of anatomical structures and abnormalities from medical images can aid in the early detection and treatment of various diseases. In this paper, we address the local feature deficiency of the Transformer model by carefully re-designing the self-attention map to produce accurate dense prediction in medical images. To this end, we first apply the wavelet transformation to decompose the input feature map into low-frequency (LF) and high-frequency (HF) subbands. The LF segment is associated with coarse-grained features while the HF components preserve fine-grained features such as texture and edge information. Next, we reformulate the self-attention operation using the efficient Transformer to perform both spatial and context attention on top of the frequency representation. Furthermore, to intensify the importance of the boundary information, we impose an additional attention map by creating a Gaussian pyramid on top of the HF components. Moreover, we propose a multi-scale context enhancement block within skip connections to adaptively model inter-scale dependencies to overcome the semantic gap among stages of the encoder and decoder modules. Throughout comprehensive experiments, we demonstrate the effectiveness of our strategy on multi-organ and skin lesion segmentation benchmarks. The implementation code will be available upon acceptance. \href{https://github.com/mindflow-institue/WaveFormer}{GitHub}.

翻译：医学图像分割是一项关键任务，在诊断、治疗规划和疾病监测中发挥着重要作用。从医学图像中准确分割解剖结构和异常区域有助于多种疾病的早期检测与治疗。本文通过重新设计自注意力机制以生成精确的密集预测，解决了Transformer模型在医学图像局部特征提取方面的不足。为此，我们首先应用小波变换将输入特征图分解为低频子带和高频子带。低频部分表征粗粒度特征，而高频分量保留纹理和边缘等细粒度特征。其次，我们利用高效Transformer重构自注意力运算，在频域表征上同时进行空间注意力和上下文注意力计算。为强化边界信息的重要性，我们进一步在高频分量上构建高斯金字塔生成附加注意力图。此外，我们在跳跃连接中提出多尺度上下文增强模块，通过自适应建模跨尺度依赖关系，克服编码器与解码器模块各阶段间的语义鸿沟。通过全面的实验，我们在多器官和皮肤病变分割基准上验证了该策略的有效性。相关实现代码将在论文接收后公开。GitHub链接：\href{https://github.com/mindflow-institue/WaveFormer}{GitHub}。