A Domain Translation Framework with an Adversarial Denoising Diffusion Model to Generate Synthetic Datasets of Echocardiography Images

arXiv:2403.04612v1 Announce Type: cross
Abstract: Currently, medical image domain translation operations show a high demand from researchers and clinicians. Amongst other capabilities, this task allows the generation of new medical images with sufficiently high image quality, making them clinically relevant. Deep Learning (DL) architectures, most specifically deep generative models, are widely used to generate and translate images from one domain to another. The proposed framework relies on an adversarial Denoising Diffusion Model (DDM) to synthesize echocardiography images and perform domain …

abstract adversarial arxiv capabilities clinicians cs.ai cs.cv datasets deep learning demand denoising diffusion diffusion model domain eess.iv framework generate image images making medical operations quality researchers show synthetic them translation type