通过聚类分块提升视觉状态空间模型的局部与全局特征融合能力
Merging Context Clustering with Visual State Space Models for Medical Image Segmentation
- 引入上下文聚类模块，动态划分图像块以保留局部依赖
- 在5个公开医学影像数据集上超越现有最先进方法
- 适合需要精细分割的医疗图像分析场景
医学图像分割需同时整合全局与局部特征表示，现有方法难以兼顾长程与短程特征交互。近期视觉状态空间模型（ViM）凭借线性复杂度在长程特征迭代中表现优异，但其直接展平空间标记会忽略局部依赖，且固定扫描模式限制了动态空间上下文的捕捉。为此，本文提出一种简单高效的上下文聚类视觉状态空间模型（CCViM），在原有ViM结构中嵌入上下文聚类模块，将图像标记分组为可变窗口，实现自适应局部聚类。该方法有效融合长程与短程特征交互，显著增强空间上下文表征能力。在Kumar、CPM17、ISIC17、ISIC18和Synapse五个公开数据集上的大量实验表明，本方法优于当前主流方法。代码已开源：https://github.com/zymissy/CCViM。
原文摘要 · Abstract (English)
Medical image segmentation demands the aggregation of global and local feature representations, posing a challenge for current methodologies in handling both long-range and short-range feature interactions. Recently, vision mamba (ViM) models have emerged as promising solutions for addressing model complexities by excelling in long-range feature iterations with linear complexity. However, existing ViM approaches overlook the importance of preserving short-range local dependencies by directly flattening spatial tokens and are constrained by fixed scanning patterns that limit the capture of dynamic spatial context information. To address these challenges, we introduce a simple yet effective method named context clustering ViM (CCViM), which incorporates a context clustering module within the existing ViM models to segment image tokens into distinct windows for adaptable local clustering. Our method effectively combines long-range and short-range feature interactions, thereby enhancing spatial contextual representations for medical image segmentation tasks. Extensive experimental evaluations on diverse public datasets, i.e., Kumar, CPM17, ISIC17, ISIC18, and Synapse demonstrate the superior performance of our method compared to current state-of-the-art methods. Our code can be found at https://github.com/zymissy/CCViM.
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