Swin Unet

Swin U-Net is a type of convolutional neural network architecture that leverages the strengths of both convolutional neural networks (CNNs) and Swin transformers to improve image segmentation and related tasks. Current research focuses on adapting Swin U-Net for diverse applications, including medical image analysis (e.g., organ segmentation, dose prediction in radiation oncology), remote sensing (e.g., lake area monitoring), and image restoration (e.g., analog video enhancement). These advancements demonstrate Swin U-Net's versatility and potential to improve accuracy and efficiency in various fields, impacting both scientific understanding and practical applications.