EndoVAE: Generating Endoscopic Images with a Variational Autoencoder

The generalization performance of deep learning models is closely associated with the number and diversity of data available upon training. While in many applications there is a large number of data available in public, in domains such as medical image analysis, the data availability is limited. This can be largely attributed to data privacy legislations, including the General Data Protection Regulation (GDPR), and the cost of data annotation by experts. Aiming to address this issue, data augmentation approaches employing deep generative models have emerged. Existing augmentation techniques are primarily based on Generative Adversarial Networks (GANs). However, ill-posed training issues of GANs such as nonconvergence, mode collapse and instability in conjunction with their demand for large scale training datasets, complicate their use in medical imaging modalities. Motivated by these issues, this paper investigates the performance of alternative generative models i.e., Variational Autoencoders (VAEs) in endoscopic image synthesis tasks. Contrary to the conventional GAN-based approaches that aiming at augmenting the existing endoscopic datasets the proposed methodology constitutes feasible the complete substitution of medical imaging datasets from real individuals with artificially generated ones. The experimental results obtained validate the effectiveness of the proposed methodology over the state-of-art.