Diffusion models are generative models that can transform noise distributions into complex data distributions through a series of reversible steps. In medical imaging, they are used for enhancing image resolution, denoising, translating images between different modalities, detecting anomalies, and augmenting data. These applications improve diagnostic accuracy, visualization, and patient outcomes by leveraging advanced image analysis techniques.
Fast Diffusion Models for Image-to-Image Generation
Fast-DDPM outperforms DDPM by significantly reducing the training time 5-fold and the sampling time nearly 100-fold.
Geodesic Diffusions Models for Image-to-Image Generation
Geodesic Diffusion Model (GDM) reduces training time 50-fold and sampling time 66-fold compared to DDPM, drastically improving image generation quality.
