医学图像分析中曼巴架构的综合调查：分类、分割、恢复及其更多应用

A Comprehensive Survey of Mamba Architectures for Medical Image Analysis: Classification, Segmentation, Restoration and Beyond

October 3, 2024

作者: Shubhi Bansal, Sreeharish A, Madhava Prasath J, Manikandan S, Sreekanth Madisetty, Mohammad Zia Ur Rehman, Chandravardhan Singh Raghaw, Gaurav Duggal, Nagendra Kumar

cs.AI

摘要

Mamba，作为状态空间模型的一个特例，在医学图像分析中作为一种替代基于模板的深度学习方法而备受青睐。虽然变压器是强大的架构，但存在一些缺点，包括二次计算复杂度以及无法有效处理长距离依赖关系。这种限制影响了在医学成像中对大型和复杂数据集的分析，其中存在许多空间和时间关系。相比之下，Mamba提供了一些优势，使其非常适合医学图像分析。它具有线性时间复杂度，这是对变压器的显著改进。Mamba在没有注意机制的情况下处理更长的序列，实现更快的推断并且需要更少的内存。Mamba还展现了在合并多模态数据方面的出色性能，提高了诊断准确性和患者预后。本文的组织使读者能够逐步欣赏Mamba在医学成像中的能力。我们首先定义SSM和模型的核心概念，包括S4、S5和S6，然后探讨Mamba架构，如纯Mamba、U-Net变体以及与卷积神经网络、变压器和图神经网络混合的模型。我们还涵盖了Mamba的优化、技术和调整、扫描、数据集、应用、实验结果，并以医学成像中的挑战和未来方向作结。本综述旨在展示Mamba在克服医学成像领域内现有障碍方面的变革潜力，为该领域的创新进展铺平道路。本文审查的应用于医学领域的Mamba架构的全面列表可在Github上找到。

English

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.