@inproceedings {27830,
	title = {Identification of spermatozoa by unsupervised learning from video data},
	journal = {37th Virtual Annual Meeting of the European Society of Human Reproduction and Embryology (ESHRE)},
	year = {2021},
	publisher = {Oxford University Press},
	abstract = {Identification of individual sperm is essential to assess a given sperm sample{\textquoteright}s motility behavior. Existing computer-aided systems need training data based on annotations by professionals, which is resource demanding. On the other hand, data analysed by unsupervised machine learning algorithms can improve supervised algorithms that are more stable for clinical applications. Therefore, unsupervised sperm identification can improve computer-aided sperm analysis systems predicting different aspects of sperm samples. Other possible applications are assessing kinematics and counting of spermatozoa.\&nbsp;},
	author = {Thambawita, Vajira and Trine B {Haugen} and Stensen, M.H. and Witczak, Oliwia and Hugo Lewi {Hammer} and Halvorsen, P{\r a}l and Riegler, Michael}
}
