  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.
