In the field of medical image segmentation, models based on both CNN and
Transformer have been thoroughly investigated. However, CNNs have limited
modeling capabilities for long-range dependencies, making it challenging to
exploit the semantic information within images fully. On the other hand, the
quadratic computational complexity poses a challenge for Transformers.
Recently, State Space Models (SSMs), such as Mamba, have been recognized as a
promising method. They not only demonstrate superior performance in modeling
long-range interactions, but also preserve a linear computational complexity.
Inspired by the Mamba architecture, We proposed Vison Mamba-UNetV2, the Visual
State Space (VSS) Block is introduced to capture extensive contextual
information, the Semantics and Detail Infusion (SDI) is introduced to augment
the infusion of low-level and high-level features. We conduct comprehensive
experiments on the ISIC17, ISIC18, CVC-300, CVC-ClinicDB, Kvasir, CVC-ColonDB
and ETIS-LaribPolypDB public datasets. The results indicate that VM-UNetV2
exhibits competitive performance in medical image segmentation tasks. Our code
is available at this https URL

在医学图像分割领域，CNN 和 Transformer 基于模型已经进行了深入研究。然而，CNN 对长距离依赖的建模能力有限，使得充分利用图像内的语义信息变得具有挑战性。另一方面，Transformer 的二次计算复杂性提出了挑战。最近，基于状态空间模型（SSMs）的方法，如 Mamba，被认为是一种有希望的方法。它们不仅在建模长程交互方面表现出卓越性能，而且保持了线性计算复杂性。受到 Mamba 架构的启发，我们提出了 Vision Mamba-UNetV2，引入了 Visual State Space（VSS）块来捕捉广泛的上下文信息，引入 Semantics and Detail Infusion（SDI）来增强低级和高级特征的融合。我们在 ISIC17、ISIC18、CVC-300、CVC-ClinicDB、Kvasir、CVC-ColonDB 和 ETIS-LaribPolypDB 等公共数据集上进行了全面的实验。结果表明，VM-UNetV2 在医学图像分割任务中表现出竞争力。我们的代码可在此网址获得