CycleGAN generated pneumonia chest x-ray images: Evaluation with vision transformer

Gerardo Lugo-Torres, José E. Valdez-Rodríguez, D. Peralta-Rodríguez
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Abstract

The use of generative models in image synthesis has become increasingly prevalent. Synthetic medical imaging data is of paramount importance, primarily because medical imaging data is scarce, costly, and encumbered by legal considerations pertaining to patient confidentiality. Synthetic medical images offer a potential answer to these issues. The predominant approaches primarily assess the quality of images and the degree of resemblance between these images and the original ones employed for their generation.The central idea of the work can be summarized in the question: Do the performance metrics of Frechet Inception Distance(FID) and Inception Score(IS) in the Cycle-consistent Generative Adversarial Networks (CycleGAN) model are adequate to determine how real a generated chest x-ray pneumonia image is? In this study, a CycleGAN model was employed to produce artificial images depicting 3 classes of chest x-ray pneumonia images: general(any type), bacterial, and viral pneumonia. The quality of the images were evaluated assessing and contrasting 3 criteria: performance metric of CycleGAN model, clinical assessment of respiratory experts and the results of classification of a visual transformer(ViT). The overall results showed that the evaluation metrics of the CycleGAN are insufficient to establish realism in generated medical images.
CycleGAN 生成肺炎胸部 X 光图像:使用视觉转换器进行评估
生成模型在图像合成中的应用越来越普遍。合成医学影像数据至关重要,这主要是因为医学影像数据稀缺、成本高昂,而且受到有关病人保密的法律限制。合成医学图像为解决这些问题提供了潜在的答案。主要的方法主要是评估图像的质量以及这些图像与原始图像之间的相似程度:工作的中心思想可以概括为这样一个问题:循环一致生成对抗网络(CycleGAN)模型中的弗雷谢特起始距离(FID)和起始分数(IS)这两个性能指标是否足以确定生成的胸部 X 光肺炎图像的真实程度?本研究采用 CycleGAN 模型生成人工图像,描述 3 类胸部 X 光肺炎图像:普通肺炎(任何类型)、细菌性肺炎和病毒性肺炎。对图像质量的评估有 3 个标准:CycleGAN 模型的性能指标、呼吸科专家的临床评估和视觉转换器(ViT)的分类结果。总体结果表明,CycleGAN 的评估指标不足以确定生成医学图像的真实性。
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