title: A Comprehensive Survey of Mamba Architectures for Medical Image
    Analysis: Classification, Segmentation, Restoration and Beyond
summary: 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.
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paper_authors.name: Chandravardhan Singh Raghaw
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paper_authors.name: Nagendra Kumar
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paper_title: A Comprehensive Survey of Mamba Architectures for Medical Image
    Analysis: Classification, Segmentation, Restoration and Beyond
paper_summary: 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.
paper_ai_summary: Mamba, a State Space Model variant, offers linear time complexity and efficient long-range dependency handling in medical image analysis, outperforming transformers in merging multimodal data.
paper_ai_keywords: State Space Model Mamba transformers quadratic computational complexity long-range dependencies linear time complexity multimodal data diagnosis accuracy patient outcomes S4 S5 S6 pure Mamba U-Net variants hybrid models convolutional neural networks Graph Neural Networks medical image analysis