Perceived Realism of High-Resolution Generative Adversarial Network-derived Synthetic Mammograms

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 3 1024 pixels by using images from 90 000 patients (average age, 56 years 6 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. This method was able to reveal potential sources of misalignment. A total of 117 volunteer participants (55 radiologists and 62 nonradiologists) took part in a study to assess the realism of synthetic images from GANs. Results: A quantitative evaluation of distributional alignment shows 60%-78% mutual-information score between the real and synthetic image distributions, and 80%-91% overlap in their support, which are strong indications against mode collapse. It also reveals shape misalignment as the main difference between the two distributions. Obvious artifacts were found by an untrained observer in 13.6% and 6.4% of the synthetic mediolateral oblique and craniocaudal images, respectively. A reader study demonstrated that real and synthetic images are perceptually inseparable by the majority of participants, even by trained breast radiologists. Only one out of the 117 participants was able to reliably distinguish real from synthetic images, and this study discusses the cues they used to do so. Conclusion: On the basis of these findings, it appears possible to generate realistic synthetic full-field digital mammograms by using a progressive GAN architecture up to a resolution of 1280 3 1024 pixels. Supplemental material is available for this article. (C) RSNA, 2020.

Automated summary

By using generative adversarial networks (GANs), this study explores the synthesis of realistic medical images that are difficult to distinguish from real images, even by domain experts. The results demonstrate the feasibility of generating realistic full-field digital mammograms using a progressive GAN architecture, with most participants unable to perceptually separate the real from synthetic images.