Getting the most of few data for neonatal pain assessment
In this paper we present three different learning models for the automatic assessment of neonatal pain during clinical procedures. Given that few data are publicly available for the training and evaluation of these systems, we develop solutions that try to get the most out of the data that are available. To accomplish this, we choose a convolutional neural network (CNN) architecture as the discriminator with a reduced number of trainable parameters that are used efficiently. Furthermore, we develop two solutions based on the generative adversarial network (GAN) framework in order to improve discriminator power by transforming it into a multitask classier and by training it with a combination of real and synthetic samples. Experimental results on the publicly available infant Classication Of Pain Expressions (iCOPE) database show superior results compared to previous works in the state of the art.
Information Technology and Cybersecurity
Convolutional neural networks, Generative adversarial networks, Neonatal pain assessment
Celona, Luigi, Sheryl Brahnam, and Simone Bianco. "Getting the most of few data for neonatal pain assessment." In Proceedings of the 13th EAI International Conference on Pervasive Computing Technologies for Healthcare, pp. 298-301. 2019.
ACM International Conference Proceeding Series