Authors | S. Hicks, P. Halvorsen, T. B. Haugen, J. M. Andersen, O. Witczak, K. Pogorelov, H. L. Hammer, D. Dang-Nguyen, M. Lux and M. Riegler |
Title | Predicting Sperm Motility and Morphology using Deep Learning and Handcrafted Features |
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Status | Published |
Publication Type | Proceedings, refereed |
Year of Publication | 2019 |
Conference Name | MediaEval |
Publisher | ceur ws org |
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 Key | 26929 |