  Medical image synthesis has emerged as a promising solution to address the
limited availability of annotated medical data needed for training machine
learning algorithms in the context of image-based Clinical Decision Support
(CDS) systems. To this end, Generative Adversarial Networks (GANs) have been
mainly applied to support the algorithm training process by generating
synthetic images for data augmentation. However, in the field of Wireless
Capsule Endoscopy (WCE), the limited content diversity and size of existing
publicly available annotated datasets, adversely affect both the training
stability and synthesis performance of GANs. Aiming to a viable solution for
WCE image synthesis, a novel Variational Autoencoder architecture is proposed,
namely "This Intestine Does not Exist" (TIDE). The proposed architecture
comprises multiscale feature extraction convolutional blocks and residual
connections, which enable the generation of high-quality and diverse datasets
even with a limited number of training images. Contrary to the current
approaches, which are oriented towards the augmentation of the available
datasets, this study demonstrates that using TIDE, real WCE datasets can be
fully substituted by artificially generated ones, without compromising
classification performance. Furthermore, qualitative and user evaluation
studies by experienced WCE specialists, validate from a medical viewpoint that
both the normal and abnormal WCE images synthesized by TIDE are sufficiently
realistic.
