Design and implementation of a data augmentation system based on generative adversarial networks for exanthema images (Ref: Exanthema).

Responsable: Julián Cabrera [julian.cabrera@gti.ssr.upm.es]

This Master Thesis addresses the problem of the lack of annotated data to train deep-learning-based algorithms in different areas of research. In this case we focus on a system to assist the diagnosis of skin problems in children that is being developed by the Grupo de Tratamiento de Imágenes. The objective of this Master Thesis is to evaluate the use of generative adversarial networks to create new synthetic data samples as a data augmentation approach. This Master Thesis will be done in collaboration with Hospital 12 de Octubre. We are looking for students with experience with Python and Deep Learning Tools ( Keras, Tensorflow, PyTorch, …).