# GAN-Based Approaches for Generating Structured Data in the Medical Domain
## 1 Introduction
The architecture of a GAN model in its original form is illustrated in Figure 1. The generator typically generates data from initially random patterns. These generated data are fed into the discriminator along with real data. The discriminator acts as a classifier. It is trained to validate the authenticity of the input data, i.e., to distinguish real data from generated data. The crucial point is that the generator only interacts with the discriminator and has no direct access to real data. The generator thus learns from its failure and improves its performance in generating realistic data through the training process (backpropagation). The two networks contest with each other in a zero-sum game; hence, their goals are adversarial. Provided that the generator is sufficiently trained, it generates artificial data that mimics the real ones, and can eventually fool the discriminator, i.e., the generated data are recognized as real by the discriminator. GANs have also attracted a lot of interest in the medical field to generate synthetic data for various applications, such as data augmentation for patient data collected from  
Figure 1: The architecture of a GAN model: Two adversarial networks are trained together. The generator is trained to generate new realistic data that are indistinguishable from real data, while the discriminator determines whether the data are real or generated. Internet of Medical Things devices, as the process of data collection can run into trouble for various reasons and cause problems for patient monitoring, and ultimately for clinical decision-making systems [28]. GAN-based models have also been used for the fast MRI (magnetic resonance imaging) reconstruction of blurry scans [29], as well as the segmentation of meibomian glands from infrared images, i.e., automatically identifying the area of meibomian glands [30], and style transfer from UBM (Ultrasound Biomicroscopy) to AS-OCT (Anterior Segment Optical Coherence Tomography) in ophthalmology image domains [31]. VAE-GAN models further combine the architecture of both methods, i.e., they use a GAN-like architecture where the discriminator is a variational autoencoder [32]. Variations of VAE-GAN models were developed, for example, for unsupervised anomaly detection and segmentation in MRI scans [33], and for generating three-dimensional brain MRIs from random patterns [34]. GANs are typically known for their success in generating realistic images, and have been extensively studied for their applicability to image and video synthesis in a wide range of applications [35; 36; 37]. However, they can be extended to account for other forms of data such as structured (tabular) data, which are the most commonly used data type, particularly in healthcare and medical applications [38]. While GANs have shown impressive results in generating images and also text data in natural language [39; 40], their performance on tabular data still remains a challenge [15]. This is more evident in the medical domain, for example, with a smaller amount of available data due to privacy issues [41; 42]. In this paper, we are concerned with the generation of structured (tabular) synthetic data for applications in the medical field. Other data types such as (2D and 3D) images and genetic data are beyond the scope of this work, and will be considered in future work. We consider various GAN-based models that are most relevant to structured data, and investigate how they can efficiently work with structured data and generate high quality synthetic tabular data suitable for medical applications. The main goal is to use synthetic data from different GANs for training binary (malignant/benign) classifiers, and to compare their performance in terms of classification accuracy with cases where only real data are considered. We aim to investigate how synthetic data improve binary classification accuracy, especially when a small amount of data is available. This is of great importance for medical applications, such as rare diseases for which only limited data are provided.