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Medical image perception research is clearly important, but it is difficult for researchers to use authentic medical images as stimuli in a controlled manner. On the one hand, public medical image datasets are relatively uncommon, often incomplete, and the data processing and labeling required for real images can be prohibitively time-consuming. On the other hand, it is hard to find medical images which have the desired experimental attributes (eg, lesion types, locations, etc.). Therefore, the stimuli that are used for medical perception experiments are often highly artificial. While these stimuli are easily generated and manipulated, they are routinely critiqued for being obviously unrealistic. Thus, generating authentic looking (ie, metameric) medical stimuli is important for medical image perception research. Here, we used the Generative Adversarial Network (GAN) to create perceptually authentic medical images. For …