Mamba, a special case of the State Space Model, is gaining popularity as an alternative to template-based deep learning approaches in medical image analysis. While transformers are powerful architectures, they have drawbacks, including quadratic computational complexity and an inability to address long-range dependencies efficiently. This limitation affects the analysis of large and complex datasets in medical imaging, where there are many spatial and temporal relationships. In contrast, Mamba offers benefits that make it well-suited for medical image analysis. It has linear time complexity, which is a significant improvement over transformers. Mamba processes longer sequences without attention mechanisms, enabling faster inference and requiring less memory. Mamba also demonstrates strong performance in merging multimodal data, improving diagnosis accuracy and patient outcomes. The organization of this paper allows readers to appreciate the capabilities of Mamba in medical imaging step by step. We begin by defining core concepts of SSMs and models, including S4, S5, and S6, followed by an exploration of Mamba architectures such as pure Mamba, U-Net variants, and hybrid models with convolutional neural networks, transformers, and Graph Neural Networks. We also cover Mamba optimizations, techniques and adaptations, scanning, datasets, applications, experimental results, and conclude with its challenges and future directions in medical imaging. This review aims to demonstrate the transformative potential of Mamba in overcoming existing barriers within medical imaging while paving the way for innovative advancements in the field. A comprehensive list of Mamba architectures applied in the medical field, reviewed in this work, is available at Github.

翻译：Mamba作为状态空间模型的特例，在医学图像分析领域正逐渐成为基于模板的深度学习方法的流行替代方案。尽管Transformer架构功能强大，但其存在二次计算复杂度及无法高效处理长程依赖等缺陷。这一局限影响了医学成像中大型复杂数据集的分析，因为此类数据包含大量时空关联。相比之下，Mamba具备更适合医学图像分析的优势特性：其线性时间复杂度较Transformer有显著改进；无需注意力机制即可处理更长序列，实现更快推理速度并降低内存需求；在融合多模态数据方面表现优异，有助于提升诊断准确性与患者预后效果。本文通过渐进式组织结构，引导读者逐步理解Mamba在医学成像中的应用潜力。首先阐释状态空间模型及S4、S5、S6等核心模型概念，继而系统探讨纯Mamba架构、U-Net变体、以及与卷积神经网络、Transformer和图神经网络的混合模型。同时涵盖Mamba优化技术、自适应方法、扫描策略、数据集、应用场景、实验结果，最终展望其在医学成像领域的挑战与未来发展方向。本综述旨在揭示Mamba突破医学成像现有技术壁垒的变革潜力，并为该领域的创新进展开辟道路。本文涉及的医学领域Mamba架构完整列表已发布于Github平台。