窗口(计算)
卷积(计算机科学)
计算机科学
分割
人工智能
计算机视觉
图像(数学)
业务
万维网
人工神经网络
作者
Jinhong Wang,Jintai Chen,Danny Chen,Junfeng Wu
出处
期刊:Cornell University - arXiv
日期:2024-03-12
标识
DOI:10.48550/arxiv.2403.07332
摘要
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 Window-based Mamba U}-shape Network, or LMa-UNet, for 2D and 3D medical image segmentation. A distinguishing feature of our LMa-UNet is its utilization of large windows, excelling in locally spatial modeling compared to small kernel-based CNNs and small window-based 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 the global and neighborhood spatial modeling capability of Mamba. Comprehensive experiments demonstrate the effectiveness and efficiency of our method and the feasibility of using large window size to achieve large receptive fields. Codes are available at https://github.com/wjh892521292/LMa-UNet.
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