Recently, State Space Models (SSMs), with Mamba as a prime example, have
shown great promise for long-range dependency modeling with linear complexity.
Then, Vision Mamba and the subsequent architectures are presented successively,
and they perform well on visual tasks. The crucial step of applying Mamba to
visual tasks is to construct 2D visual features in sequential manners. To
effectively organize and construct visual features within the 2D image space
through 1D selective scan, we propose a novel Multi-Head Scan (MHS) module. The
embeddings extracted from the preceding layer are projected into multiple
lower-dimensional subspaces. Subsequently, within each subspace, the selective
scan is performed along distinct scan routes. The resulting sub-embeddings,
obtained from the multi-head scan process, are then integrated and ultimately
projected back into the high-dimensional space. Moreover, we incorporate a Scan
Route Attention (SRA) mechanism to enhance the module's capability to discern
complex structures. To validate the efficacy of our module, we exclusively
substitute the 2D-Selective-Scan (SS2D) block in VM-UNet with our proposed
module, and we train our models from scratch without using any pre-trained
weights. The results indicate a significant improvement in performance while
reducing the parameters of the original VM-UNet. The code for this study is
publicly available at this https URL

提出了一种基于 State Space Models 的 Multi-Head Scan (MHS) 模块，通过 1D selective scan 在 2D 图像空间内构建视觉特征，结合了 Scan Route Attention (SRA) 机制来提高模块的复杂结构辨识能力，并在实验证明了该方法在性能上的显著改善与参数减少。

MHS-VM：多头并行子空间扫描用于视觉玛巴

MHS-VM: Multi-Head Scanning in Parallel Subspaces for Vision Mamba

In the field of medical image segmentation, variant models based on
Convolutional Neural Networks (CNNs) and Visual Transformers (ViTs) as the base
modules have been very widely developed and applied. However, CNNs are often
limited in their ability to deal with long sequences of information, while the
low sensitivity of ViTs to local feature information and the problem of
secondary computational complexity limit their development. Recently, the
emergence of state-space models (SSMs), especially 2D-selective-scan (SS2D),
has had an impact on the longtime dominance of traditional CNNs and ViTs as the
foundational modules of visual neural networks. In this paper, we extend the
adaptability of SS2D by proposing a High-order Vision Mamba UNet (H-vmunet) for
medical image segmentation. Among them, the proposed High-order
2D-selective-scan (H-SS2D) progressively reduces the introduction of redundant
information during SS2D operations through higher-order interactions. In
addition, the proposed Local-SS2D module improves the learning ability of local
features of SS2D at each order of interaction. We conducted comparison and
ablation experiments on three publicly available medical image datasets
(ISIC2017, Spleen, and CVC-ClinicDB), and the results all demonstrate the
strong competitiveness of H-vmunet in medical image segmentation tasks. The
code is available from this https URL .

该研究设计了一种基于状态空间模型的高阶视觉 Mamba UNet (H-vmunet) 用于医学图像分割，其中提出的高阶 2D 选择性扫描 (H-SS2D) 模块通过更高阶的交互逐渐减少冗余信息的引入，提高了 2D 选择性扫描操作的适应性，并且所提出的局部 SS2D 模块改进了每个交互阶段中局部特征的学习能力。通过对三个公开可用的医学图像数据集（ISIC2017、Spleen 和 CVC-ClinicDB）进行对比和消融实验，研究结果显示了 H-vmunet 在医学图像分割任务中的强大竞争力。