Papers › CSWin-UNet: Transformer UNet with Cross-Shaped Windows for Medical Image Segmentation

CSWin-UNet: Transformer UNet with Cross-Shaped Windows for Medical Image Segmentation

25 Jul 2024arXiv:2407.18070archive 2025-07-28

Xiao Liu, Peng Gao, Tao Yu, Fei Wang, Ru-Yue Yuan

Deep learning, especially convolutional neural networks (CNNs) and Transformer architectures, have become the focus of extensive research in medical image segmentation, achieving impressive results. However, CNNs come with inductive biases that limit their effectiveness in more complex, varied segmentation scenarios. Conversely, while Transformer-based methods excel at capturing global and long-range semantic details, they suffer from high computational demands. In this study, we propose CSWin-UNet, a novel U-shaped segmentation method that incorporates the CSWin self-attention mechanism into the UNet to facilitate horizontal and vertical stripes self-attention. This method significantly enhances both computational efficiency and receptive field interactions. Additionally, our innovative decoder utilizes a content-aware reassembly operator that strategically reassembles features, guided by predicted kernels, for precise image resolution restoration. Our extensive empirical evaluations on diverse datasets, including synapse multi-organ CT, cardiac MRI, and skin lesions, demonstrate that CSWin-UNet maintains low model complexity while delivering high segmentation accuracy. Codes are available at https://github.com/eatbeanss/CSWin-UNet.

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Computational Efficiency Decoder Image Segmentation Medical Image Segmentation Segmentation Semantic Segmentation

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Absolute Position Encodings Adam Attention BPE Dense Connections Dropout Focus Label Smoothing Layer Normalization Linear Layer Multi-Head Attention Position-Wise Feed-Forward Layer Residual Connection Softmax Transformer

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