@misc{15549,
  author = {Steven Hicks and P{\r a}l Halvorsen and Trine Haugen and Jorunn Andersen and Oliwia Witczak and Konstantin Pogorelov and Hugo Hammer and Duc-Tien Dang-Nguyen and Mathias Lux and Michael Riegler},
  title = {Predicting Sperm Motility and Morphology using Deep Learning and Handcrafted Features},
  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.},
  year = {2019},
  journal = {MediaEval},
  publisher = {ceur ws org},
}