|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|
|Project(s)||Department of Holistic Systems|
|Publication Type||Proceedings, refereed|
|Year of Publication||2019|
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.