论文标题
使用儿科WBMRI的生成模型
Using Generative Models for Pediatric wbMRI
论文作者
论文摘要
癌症的早期发现是良好预后的关键，需要经常进行测试，尤其是在儿科中。全身磁共振成像（WBMRI）是几种建立良好筛选方案的重要组成部分，筛查从幼儿开始。迄今为止，机器学习（ML）已在WBMRI图像上用于成人癌症患者。由于整个生长过程中的骨骼信号变化，由于运动和有限的依从性而在幼儿中获得这些图像的困难以及阳性病例的稀有性，因此无法在儿科中使用此类工具。我们评估了使用来自多伦多病假儿童医院的WBMRI数据培训的生成对抗网络（GAN）生成的WBMRI图像的质量。我们使用frchet Inception距离（FID）度量，域FRCHET距离（DFD）和放射学研究员进行盲测。我们证明，stylegan2在生成WBMRI图像相对于所有三个指标方面提供了最佳性能。
Early detection of cancer is key to a good prognosis and requires frequent testing, especially in pediatrics. Whole-body magnetic resonance imaging (wbMRI) is an essential part of several well-established screening protocols, with screening starting in early childhood. To date, machine learning (ML) has been used on wbMRI images to stage adult cancer patients. It is not possible to use such tools in pediatrics due to the changing bone signal throughout growth, the difficulty of obtaining these images in young children due to movement and limited compliance, and the rarity of positive cases. We evaluate the quality of wbMRI images generated using generative adversarial networks (GANs) trained on wbMRI data from The Hospital for Sick Children in Toronto. We use the Frchet Inception Distance (FID) metric, Domain Frchet Distance (DFD), and blind tests with a radiology fellow for evaluation. We demonstrate that StyleGAN2 provides the best performance in generating wbMRI images with respect to all three metrics.
