Dataset_for_review

Machine learning (ML) in the medical domain faces challenges due to limited high-quality data. This study addresses the scarcity of echocardiography images (echoCG) by generating synthetic data using state-of-the-art generative models. We evaluated a cycle-consistent generative adversarial network (CycleGAN), contrastive unpaired translation (CUT) method, and latent diffusion model (Stable Diffusion 1.5). Our methodology, image samples, and evaluation strategy are detailed, including a user study with cardiologists and surgeons assessing the quality and medical soundness of the generated samples. This research highlights the potential of synthetic data to improve ML applications in healthcare, enhancing diagnostic accuracy and patient outcomes.