@inproceedings {26923,
	title = {Extracting temporal features into a spatial domain using autoencoders for sperm video analysis},
	journal = {MediaEval 2019},
	year = {2019},
	month = {10/2019},
	publisher = {CEUR Workshop Proceedings},
	abstract = {In this paper, we present a two-step deep learning method that is used to predict sperm motility and morphology based on video recordings of human spermatozoa. First, we use an autoencoder to extract temporal features from a given semen video and plot these into image-space, which we call feature-images. Second, these feature-images are used to perform transfer learning to predict the motility and morphology values of human sperm. The presented method shows it{\textquoteright}s capability to extract temporal information into spatial domain feature-images which can be used with traditional convolutional neural networks. Furthermore, the accuracy of the predicted motility of a given semen sample shows that a deep learning-based model can capture the temporal information of microscopic recordings of human semen.},
	author = {Thambawita, Vajira and Halvorsen, P{\r a}l and Hugo Lewi {Hammer} and Riegler, Michael and Trine B. {Haugen}}
}
