Attention UNet
Attention UNet architectures represent a significant advancement in image segmentation, particularly within medical imaging and remote sensing, aiming to improve accuracy and efficiency by incorporating attention mechanisms into the established UNet framework. Current research focuses on enhancing these models through various strategies, including 3D adaptations, the integration of transformer networks and other attention modules (e.g., convolutional block attention, squeeze-and-excitation), and optimization for specific challenges like small target segmentation or class imbalance. These improvements lead to more precise and reliable segmentation results, impacting applications ranging from disease diagnosis and treatment planning to weather forecasting and environmental monitoring.