對於醫學影像分析的 Mamba 架構全面調查：分類、分割、修復及更多。

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是狀態空間模型的一個特殊案例，作為醫學影像分析中基於模板的深度學習方法的替代方案，正變得越來越受歡迎。儘管變壓器是強大的架構，但存在缺點，包括二次計算複雜度和無法有效處理長距離依賴性。這一限制影響了在醫學影像中分析大型和複雜數據集，其中存在許多空間和時間關係。相比之下，Mamba提供了使其非常適合醫學影像分析的優勢。它具有線性時間複雜度，這是對變壓器的一個重大改進。Mamba處理更長序列而無需注意機制，實現更快的推斷並且需要更少的內存。Mamba還展現了在合併多模態數據方面的強大性能，提高了診斷準確性和患者結果。本文的組織使讀者能夠逐步欣賞Mamba在醫學影像中的能力。我們首先定義SSM和模型的核心概念，包括S4、S5和S6，然後探索Mamba架構，如純Mamba、U-Net變體以及與卷積神經網絡、變壓器和圖神經網絡的混合模型。我們還涵蓋了Mamba的優化、技術和適應、掃描、數據集、應用、實驗結果，並最終討論了在醫學影像中的挑戰和未來方向。本綜述旨在展示Mamba在克服醫學影像中現有障礙方面的轉型潛力，同時為該領域的創新進展鋪平道路。本文中檢視的應用於醫學領域的Mamba架構的全面列表可在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.