stable diffusion.
![](https://ieee-dataport.org/sites/default/files/styles/3x2/public/tags/images/artificial-intelligence-3382521_1920.jpg?itok=Vz0tijaY)
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).
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