LKM-UNet: Large Kernel Vision Mamba UNet for Medical Image Segmentation
This work addresses medical image segmentation for clinical practice, offering an incremental improvement by combining Mamba with large kernels to enhance spatial modeling efficiency.
The paper tackled the problem of limited receptive fields and costly long-range modeling in medical image segmentation by introducing LKM-UNet, which uses large Mamba kernels to achieve large receptive fields with linear complexity, demonstrating feasibility and effectiveness in experiments.
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.