MSLAU-Net: A Hybird CNN-Transformer Network for Medical Image Segmentation
This work addresses segmentation challenges in medical imaging by combining CNN and Transformer strengths, though it is incremental as it builds on existing hybrid approaches.
The paper tackled the problem of medical image segmentation by proposing MSLAU-Net, a hybrid CNN-Transformer architecture that integrates multi-scale linear attention and a top-down feature aggregation mechanism, achieving state-of-the-art performance on benchmark datasets across three imaging modalities.
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.