Swin Unet TRansformer

Swin U-Net Transformers (Swin UNETR) are a class of deep learning models combining the strengths of U-Net architectures for image segmentation with the long-range dependency modeling capabilities of Swin Transformers. Current research focuses on optimizing decoder designs within Swin UNETR to improve segmentation accuracy and detail, particularly in medical imaging applications like brain tumor segmentation and precipitation nowcasting. These models are proving valuable for various tasks, including medical image analysis, astronomical image processing, and geophysical data interpretation, offering improved accuracy and efficiency compared to traditional methods.