  Medical image synthesis has gained a great focus recently, especially after
the introduction of Generative Adversarial Networks (GANs). GANs have been used
widely to provide anatomically-plausible and diverse samples for augmentation
and other applications, including segmentation and super resolution. In our
previous work, Deep Convolutional GANs were used to generate synthetic
mammogram lesions, masses mainly, that could enhance the classification
performance in imbalanced datasets. In this new work, a deeper investigation
was carried out to explore other aspects of the generated images evaluation,
i.e., realism, feature space distribution, and observers studies. t-Stochastic
Neighbor Embedding (t-SNE) was used to reduce the dimensionality of real and
fake images to enable 2D visualisations. Additionally, two expert radiologists
performed a realism-evaluation study. Visualisations showed that the generated
images have a similar feature distribution of the real ones, avoiding outliers.
Moreover, Receiver Operating Characteristic (ROC) curve showed that the
radiologists could not, in many cases, distinguish between synthetic and real
lesions, giving 48% and 61% accuracies in a balanced sample set.
