Conditional diffusion models can generate realistic knee radiographs that adhere to provided segmentation guides, outperforming conventional image-to-image models.
Leveraging the joint representation of anatomical semantic label maps and text prompts, this work demonstrates the ability of diffusion-based models to generate high-fidelity and diverse synthetic echocardiography images, which can enhance the performance of downstream medical segmentation and classification tasks.