4 research outputs found
GAN-Based Approaches for Generating Structured Data in the Medical Domain
Modern machine and deep learning methods require large datasets to achieve reliable
and robust results. This requirement is often difficult to meet in the medical field, due to data
sharing limitations imposed by privacy regulations or the presence of a small number of patients (e.g.,
rare diseases). To address this data scarcity and to improve the situation, novel generative models
such as Generative Adversarial Networks (GANs) have been widely used to generate synthetic
data that mimic real data by representing features that reflect health-related information without
reference to real patients. In this paper, we consider several GAN models to generate synthetic data
used for training binary (malignant/benign) classifiers, and compare their performances in terms
of classification accuracy with cases where only real data are considered. We aim to investigate
how synthetic data can improve classification accuracy, especially when a small amount of data is
available. To this end, we have developed and implemented an evaluation framework where binary
classifiers are trained on extended datasets containing both real and synthetic data. The results show
improved accuracy for classifiers trained with generated data from more advanced GAN models,
even when limited amounts of original data are available
Comparing Generative Adversarial Network Techniques for Image Creation and Modification
Generative adversarial networks (GANs) have demonstrated to be successful at generating realistic real-world images. In this paper we compare various GAN techniques, both supervised and unsupervised. The effects on training stability of different objective functions are compared. We add an encoder to the network, making it possible to encode images to the latent space of the GAN. The generator, discriminator and encoder are parameterized by deep convolutional neural networks. For the discriminator network we experimented with using the novel Capsule Network, a state-of-the-art technique for detecting global features in images. Experiments are performed using a digit and face dataset, with various visualizations illustrating the results. The results show that using the encoder network it is possible to reconstruct images. With the conditional GAN we can alter visual attributes of generated or encoded images. The experiments with the Capsule Network as discriminator result in generated images of a lower quality, compared to a standard convolutional neural network