AuthorsS. Hicks, P. Halvorsen, T. B. Haugen, J. M. Andersen, O. Witczak, K. Pogorelov, H. L. Hammer, D. Dang-Nguyen, M. Lux and M. Riegler
TitlePredicting Sperm Motility and Morphology using Deep Learning and Handcrafted Features
AfilliationMachine Learning
Project(s)Department of Holistic Systems
StatusAccepted
Publication TypeProceedings, refereed
Year of Publication2019
Conference NameMediaEval
Abstract

This paper presents the approach proposed by the organizer team (SimulaMet) for MediaEval 2019 Multimedia for Medicine: The Medico Task. The approach uses a data preparation method which is based on global features extracted from multiple frames within each video and then combines this with information about the patient in order to create a compressed representation of each video. The goal is to create a less hardware expensive data representation that still retains the temporal information of the video and related patient data. Overall, the results need some improvement before being a viable option for clinical use.

Citation Key26929