How to increase and balance current DBT datasets via an Evolutionary GAN: preliminary results

Deep learning techniques have led to a vast improve-ment in various fields of computer vision, mainly using large-scale labelled datasets. Obtaining a large dataset of medical images for diagnostics is still an open challenge; the most significant obstacles are data imbalances and privacy issues related to sensitive patient information. Furthermore, the limited size of datasets for the training of a neural network can affect the performance of supervised learning and cause model overfitting problems. For this reason, data augmentation techniques are used to expand existing datasets. Generative Adversarial Networks (GANs) represent an innovative solution for acquiring additional information from a dataset since they can generate synthetic samples indistinguishable from real sample images. This work explores the use of GAN networks on Digital Breast Tomosynthe-sis (DBT) images, which is, in our knowledge, a completely new approach in this domain. In particular, we apply an optimization approach to the learning process of GAN networks based on evolutionary techniques.