Evaluation of Modern Generative Networks for EchoCG Image Generation

The applications of machine learning (ML) in the medical domain are often hindered by the limited availability of high-quality data. To address this challenge, we explore the synthetic generation of echocardiography images (echoCG) using state-of-the-art generative models. We conduct a comprehensive evaluation of three prominent methods: Cycle-consistent generative adversarial network (CycleGAN), Contrastive Unpaired Translation (CUT), and Stable Diffusion 1.5 with Low-Rank Adaptation (LoRA). Our research presents the data generation methodology, image samples, and evaluation strategy, followed by an extensive user study involving licensed cardiologists and surgeons who assess the perceived quality and medical soundness of the generated images. Our findings indicate that Stable Diffusion outperforms both CycleGAN and CUT in generating images that are nearly indistinguishable from real echoCG images, making it a promising tool for augmenting medical datasets. However, we also identify limitations in the synthetic images generated by CycleGAN and CUT, which are easily distinguishable as non-realistic by medical professionals. This study highlights the potential of diffusion models in medical imaging and their applicability in addressing data scarcity, while also outlining the areas for future improvement.