Generative Adversarial Networks for Biomedical Imaging

Abstract

GANs are a class of machine learning framework that are used to generate new data instances that resemble the training data. First proposed by Goodfellow et al.,1 the GAN architecture (Figure 1) typically consists of two separate competing adversarial neural networks that learn from each other. The two neural networks in a GAN model are a generator model, which creates new synthetic data samples, and a discriminator model, which evaluates the synthetic samples generated by the generator model against real data samples. The evaluation of synthetic data by the discriminator helps the generator create better and more realistic, accurate samples. As the generator improves, so does the discriminator, enabling effective learning with the aim of producing synthetic data that are so realistic that the discriminator cannot tell if they are real or fake.