TM-UNet：用于高效医学图像分割的令牌-记忆增强序列建模
TM-UNet: Token-Memory Enhanced Sequential Modeling for Efficient Medical Image Segmentation
AI总结：
针对基于Transformer的医学图像分割方法计算成本高的问题，提出轻量级框架TM-UNet，通过多尺度令牌-记忆块实现线性复杂度的长程依赖捕获，在降低计算成本的同时优于现有方法。
摘要：
医学图像分割对临床诊断和治疗规划至关重要。尽管基于Transformer的方法已取得显著成果，但其高计算成本阻碍了临床部署。为解决这一问题，我们提出TM-UNet，这是一种新颖的轻量级框架，将令牌序列建模与高效记忆机制集成，以实现高效医学分割。具体而言，我们引入多尺度令牌-记忆（MSTM）块，通过策略性空间扫描将2D空间特征转换为令牌序列，利用矩阵记忆单元选择性保留和传播令牌间的判别性上下文信息。这种新颖的令牌-记忆机制作为动态知识存储，以线性复杂度捕获长程依赖，实现高效全局推理而无冗余计算。我们的MSTM块进一步整合指数门控以识别令牌有效性，并通过并行池化操作进行多尺度上下文提取，在无计算开销的情况下实现分层表示学习。大量实验表明，TM-UNet在多种医学分割任务上优于最先进方法，且计算成本显著降低。代码可在https://github.com/xq141839/TM-UNet获取。
英文摘要：
Medical image segmentation is essential for clinical diagnosis and treatment planning. Although transformer-based methods have achieved remarkable results, their high computational cost hinders clinical deployment. To address this issue, we propose TM-UNet, a novel lightweight framework that integrates token sequence modeling with an efficient memory mechanism for efficient medical segmentation. Specifically, we introduce a multi-scale token-memory (MSTM) block that transforms 2D spatial features into token sequences through strategic spatial scanning, leveraging matrix memory cells to selectively retain and propagate discriminative contextual information across tokens. This novel token-memory mechanism acts as a dynamic knowledge store that captures long-range dependencies with linear complexity, enabling efficient global reasoning without redundant computation. Our MSTM block further incorporates exponential gating to identify token effectiveness and multi-scale contextual extraction via parallel pooling operations, enabling hierarchical representation learning without computational overhead. Extensive experiments demonstrate that TM-UNet outperforms state-of-the-art methods across diverse medical segmentation tasks with substantially reduced computation cost. The code is available at https://github.com/xq141839/TM-UNet.
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。
