MSLAU-Net: A Hybird CNN-Transformer Network for Medical Image Segmentation Article Swipe

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Libin Lan , Yanxin Li , Xiaojuan Liu , Juan Zhou , Jianxun Zhang , Nannan Huang , Yudong Zhang · YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2505.18823

Both CNN-based and Transformer-based methods have achieved remarkable success in medical image segmentation tasks. However, CNN-based methods struggle to effectively capture global contextual information due to the inherent limitations of convolution operations. Meanwhile, Transformer-based methods suffer from insufficient local feature modeling and face challenges related to the high computational complexity caused by the self-attention mechanism. To address these limitations, we propose a novel hybrid CNN-Transformer architecture, named MSLAU-Net, which integrates the strengths of both paradigms. The proposed MSLAU-Net incorporates two key ideas. First, it introduces Multi-Scale Linear Attention, designed to efficiently extract multi-scale features from medical images while modeling long-range dependencies with low computational complexity. Second, it adopts a top-down feature aggregation mechanism, which performs multi-level feature aggregation and restores spatial resolution using a lightweight structure. Extensive experiments conducted on benchmark datasets covering three imaging modalities demonstrate that the proposed MSLAU-Net outperforms other state-of-the-art methods on nearly all evaluation metrics, validating the superiority, effectiveness, and robustness of our approach. Our code is available at https://github.com/Monsoon49/MSLAU-Net.

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preprint

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en

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http://arxiv.org/abs/2505.18823

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https://doi.org/10.48550/arxiv.2505.18823

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MSLAU-Net: A Hybird CNN-Transformer Network for Medical Image Segmentation

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preprint

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en

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2025

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2025-05-24

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Libin Lan, Yanxin Li, Xiaojuan Liu, Juan Zhou, Jianxun Zhang, Nannan Huang, Yudong Zhang

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https://arxiv.org/abs/2505.18823

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green

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1

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2025: 1

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