Diffusion Segmentation

Diffusion segmentation leverages the power of denoising diffusion probabilistic models to perform image segmentation, aiming for accurate and robust delineation of regions of interest within images. Current research focuses on improving model architectures, such as incorporating transformers and U-Net structures, and enhancing semantic understanding through text-guided and morphology-driven learning to address challenges like limited labeled data and complex image structures. This approach shows promise across diverse applications, including medical image analysis (e.g., lesion detection, organ segmentation) and cultural heritage preservation (e.g., damage detection in artwork), offering improved accuracy and uncertainty quantification compared to traditional methods.