Generation of Synthetic Data

Abstract

Artificial intelligence is growing, but techniques like deep learning require more data than is usually available, especially in the medical context. Usually, the available data sets are not representative of reality, meaning that more samples have to be acquired, which is very costly. The demand for tools that can generate as much data as needed has increased. Traditional data augmentation tools are used to expand the available data, but they are not able to generate new data. The use of generative adversarial networks to generate synthetic data has proven revolutionary for big data as it increases the amount of available data without much cost. To this end, an adaptation of alpha-GAN for 3D MRI scans was developed to create a pipeline for generating as many synthetic scans of rat brains as needed. The applicability of the synthetic data was tested in a segmentation test and the realism by visual assessment.