Radiol Artif Intell 2021 Mar 23;3(2):e190181. Epub 2020 Dec 23.
Kheiron Medical Technologies Ltd, 116 Old Street, London EC1V 9BG, England (D.K., H.H., A.H., E.K., G.W., T.R., P.K.); and Department of Computing, Imperial College London, London, England (B.G.).
Purpose: To explore whether generative adversarial networks (GANs) can enable synthesis of realistic medical images that are indiscernible from real images, even by domain experts.
Materials And Methods: In this retrospective study, progressive growing GANs were used to synthesize mammograms at a resolution of 1280 × 1024 pixels by using images from 90 000 patients (average age, 56 years ± 9) collected between 2009 and 2019. To evaluate the results, a method to assess distributional alignment for ultra-high-dimensional pixel distributions was used, which was based on moment plots. Read More