AuthorsJ. Rosenblad, S. Hicks, H. K. Stensland, T. B. Haugen, P. Halvorsen and M. Riegler
TitleUsing 2D and 3D Convolutional Neural Networks to Predict Semen Quality
AfilliationMachine Learning
Project(s)Department of Holistic Systems
StatusPublished
Publication TypeProceedings, refereed
Year of Publication2019
Conference NameMediaEval
PublisherCEUR Workshop Proceedings
Abstract

In this paper, we present the approach of team Jmag to solve this year's Medico Multimedia Task as part of the MediaEval 2019 Benchmark. This year, the task focuses on automatically determining quality characteristics of human sperm through the analysis of microscopic videos of human semen and associated patient data. Our approach is based on deep convolutional neural networks (CNNs) of varying sizes and dimensions. Here, we aim to analyze both the spatial and temporal information present in the videos. The results show that the method holds promise for predicting the motility of sperm, but predicting morphology appears to be more difficult.

Citation Key26928

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