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

2024-10-03

arXiv.org

AI Summary

To address the high computational complexity (O(N²)) and inadequate long-range dependency modeling of Transformers in medical image analysis, this work systematically reviews the application of the Mamba architecture across medical image classification, segmentation, and restoration tasks. We comprehensively categorize and analyze three classes of medical Mamba variants: pure Mamba, U-Net-enhanced Mamba, and hybrid models integrating CNNs, Transformers, or GNNs. Key innovations include optimized scanning strategies, multimodal alignment mechanisms, lightweight sequence modeling, and domain-specific adaptation to medical datasets. Experimental results demonstrate that Mamba achieves linear time complexity, reduces GPU memory consumption by 37% on average, accelerates inference by 2.1×, and matches or surpasses state-of-the-art accuracy across multiple benchmarks. This study elucidates the clinical potential and adaptation principles of state space models for efficient, scalable, and multimodal intelligent diagnosis and treatment.

Technology Category

• Computer Vision: Medical and Biological Imaging
• Machine Learning: Deep Neural Architectures and Foundation Models
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• Overcoming quadratic complexity limitations of transformers in medical imaging
• Addressing long-range dependency challenges in large medical datasets
• Improving multimodal data integration for enhanced diagnostic accuracy

Innovation

• Uses Mamba with linear time complexity
• Integrates Mamba with U-Net variants
• Combines Mamba with CNNs and transformers