In medical imaging, access to data is commonly limited due to patient privacy
restrictions and the issue that it can be difficult to acquire enough data in
the case of rare diseases.[1] The purpose of this investigation was to develop
a reusable open-source synthetic image generation pipeline, the GAN Image
Synthesis Tool (GIST), that is easy to use as well as easy to deploy. The
pipeline helps to improve and standardize AI algorithms in the digital health
space by generating high quality synthetic image data that is not linked to
specific patients. Its image generation capabilities include the ability to
generate imaging of pathologies or injuries with low incidence rates. This
improvement of digital health AI algorithms could improve diagnostic accuracy,
aid in patient care, decrease medicolegal claims, and ultimately decrease the
overall cost of healthcare. The pipeline builds on existing Generative
Adversarial Networks (GANs) algorithms, and preprocessing and evaluation steps
were included for completeness. For this work, we focused on ensuring the
pipeline supports radiography, with a focus on synthetic knee and elbow x-ray
images. In designing the pipeline, we evaluated the performance of current GAN
architectures, studying the performance on available x-ray data. We show that
the pipeline is capable of generating high quality and clinically relevant
images based on a lay person's evaluation and the Fr\'echet Inception Distance
(FID) metric.

开发可重复使用的开源合成图像生成流程，该流程可帮助提高数字健康领域中的人工智能算法并标准化，生成不与特定患者相关的高质量合成图像数据，以改善诊断准确性、协助患者护理、降低医疗法律索赔以及最终降低卫生保健总成本。