Generation of Artificial Pressure Ulcer Images Using Deep Convolutional GANs

Current Directions in Biomedical Engineering, vol. 11, pp. 278–281

Abstract

Abstract Pressure ulcers pose a threat, especially for older people and ICU patients. Data-driven approaches could allow for early detection and continuous evaluation. In this work, a deep convolutional generative adversarial network is presented which is able to generate new images of pressure ulcers. The cavities concerning the training dataset and the transferlearning strategy are analyzed to optimally supplement small clinical datasets for more robust algorithm development.

Authors 4

1. Daniel Blase Aachen Medical Information Technology (MedIT)

   RWTH Aachen University

   Affiliation as printed

   Medical Information Technology (MedIT), RWTH Aachen University, Pauwelsstraße 20, Aachen , Germany

2. Mengying Wang Aachen Medical Information Technology (MedIT)

   RWTH Aachen University

   Affiliation as printed

   Medical Information Technology (MedIT), RWTH Aachen University, Pauwelsstraße 20, Aachen , Germany

3. Steffen K. Leonhardt Aachen Medical Information Technology (MedIT)

   RWTH Aachen University

   Affiliation as printed

   Medical Information Technology (MedIT), RWTH Aachen University, Pauwelsstraße 20, Aachen , Germany

4. Markus Lueken Aachen Medical Information Technology (MedIT)

   RWTH Aachen University

   Affiliation as printed

   Medical Information Technology (MedIT), RWTH Aachen University, Pauwelsstraße 20, Aachen , Germany