CSWin-UNet: Transformer UNet with Cross-Shaped Windows for Medical Image Segmentation
This work addresses computational efficiency and segmentation accuracy for medical imaging applications, representing an incremental improvement over existing Transformer and CNN methods.
The authors tackled the problem of high computational demands in Transformer-based medical image segmentation by proposing CSWin-UNet, which integrates a cross-shaped window self-attention mechanism into a UNet architecture, achieving high segmentation accuracy with low model complexity on datasets like synapse multi-organ CT, cardiac MRI, and skin lesions.
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.