Quality analysis of DCGAN-generated mammography lesions
Basel Alyafi, Oliver Díaz, Premkumar Elangovan, Joan Carles Vilanova, Javier del Riego, Robert Martí · 2020
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 observer 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, the Receiver Operating Characteristic (ROC) study showed that the radiologists could not, in many cases, distinguish between synthetic and real lesions, giving accuracies between 51% and 59% using a balanced sample set.