  In clinical practice, medical image segmentation provides useful information
on the contours and dimensions of target organs or tissues, facilitating
improved diagnosis, analysis, and treatment. In the past few years,
convolutional neural networks (CNNs) and Transformers have dominated this area,
but they still suffer from either limited receptive fields or costly long-range
modeling. Mamba, a State Space Sequence Model (SSM), recently emerged as a
promising paradigm for long-range dependency modeling with linear complexity.
In this paper, we introduce a Large Kernel Vision Mamba U-shape Network, or
LKM-UNet, for medical image segmentation. A distinguishing feature of our
LKM-UNet is its utilization of large Mamba kernels, excelling in locally
spatial modeling compared to small kernel-based CNNs and Transformers, while
maintaining superior efficiency in global modeling compared to self-attention
with quadratic complexity. Additionally, we design a novel hierarchical and
bidirectional Mamba block to further enhance Mamba's global and neighborhood
spatial modeling capability for vision inputs. Comprehensive experiments
demonstrate the feasibility and the effectiveness of using large-size Mamba
kernels to achieve large receptive fields. Codes are available at
https://github.com/wjh892521292/LKM-UNet.
