Decoder U Net

Decoder U-Net architectures are modified U-Net models enhancing image segmentation accuracy, particularly for challenging tasks with limited training data or complex features. Current research focuses on improving decoder design, incorporating multi-scale or dual-decoder structures, and integrating attention mechanisms to refine feature extraction and reduce uncertainty. These advancements are significantly impacting various fields, including medical image analysis (e.g., organ segmentation) and remote sensing (e.g., road extraction), by improving the accuracy and reliability of automated image processing. The resulting improvements in segmentation accuracy translate to better diagnostic tools and more efficient automated systems.