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Generative adversarial networks (GANs) can synthesize various feasible looking images. We showed that a GAN, specifically, a conditional GAN (CGAN), can simulate breast mammograms with normal, healthy appearances, and can help detect mammographically-occult (MO) cancer. However, like other GANs, CGANs can suffer from various artifacts, e.g., checkerboard artifacts, that may impact the quality of the final synthesized image, as well as the performance of detecting MO cancer. In this study, we explored the types of GAN artifacts that exist in mammogram simulations and its effect on MO cancer detection. We first trained a CGAN using digital mammograms (FFDMs) of 1366 women with normal/healthy breasts. Then, we tested the trained CGAN on an independent MO cancer dataset with 333 women with dense breasts (97 MO cancer). We trained a convolutional neural network (CNN) on the MO cancer dataset, where real and simulated mammograms were fused, to identify women with MO cancer. We then randomly sampled 50 normal controls and found 11 and 7 cases with checkerboard and nipple artifacts, respectively. The mean and standard deviation score for the trained CNN for the cases with checkerboard and nipple artifacts were low, 0.236 ± 0.227 with [min, max] = [0.017, 0.761] and 0.069 ± 0.069 with [min, max] = [0.003, 0.213], respectively, showing the minimal effect of GAN artifacts on MO cancer detection.
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