Evaluation of Modern Generative Networks for EchoCG Image Generation
Rakhmetulayeva S. Zhanabekov Z. Bolshibayeva A.
2024Tech Science Press
Computers, Materials and Continua
2024#81Issue 34503 - 4523 pp.
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. Copyright
CycleGAN , generative adversarial networks , latent diffusion models , stable diffusion , synthetic echogcardiography , Synthetic image generation
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Department of Cybersecurity, Information Processing and Storage, Satbayev University, Almaty, 050000, Kazakhstan
Department of Mathematical Computer Modeling, International IT University, Almaty, 050000, Kazakhstan
Department of Information Systems, International IT University, Almaty, 050000, Kazakhstan
Department of Cybersecurity
Department of Mathematical Computer Modeling
Department of Information Systems
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