Evaluation of Modern Generative Networks for EchoCG Image Generation  

作　　者：Sabina Rakhmetulayeva Zhandos Zhanabekov Aigerim Bolshibayeva 

机构地区：[1]Department of Cybersecurity,Information Processing and Storage,Satbayev University,Almaty,050000,Kazakhstan [2]Department of Mathematical Computer Modeling,International IT University,Almaty,050000,Kazakhstan [3]Department of Information Systems,International IT University,Almaty,050000,Kazakhstan

出　　处：《Computers, Materials & Continua》2024年第12期4503-4523,共21页计算机、材料和连续体（英文）

基　　金：funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan(Grant No.AP13068032-Development of Methods and Algorithms for Machine Learning for Predicting Pathologies of the Cardiovascular System Based on Echocardiography and Electrocardiography).

摘　　要：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 methodol-ogy,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.

关 键 词：Synthetic image generation synthetic echogcardiography generative adversarial networks CycleGAN latent diffusion models stable diffusion 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]