@misc {27862,
	title = {Assessment of sperm motility according to WHO classification using convolutional neural networks},
	year = {2021},
	publisher = {ESHRE},
	address = {ESHRE},
	author = {Trine B. {Haugen} and Hicks, Steven and Witczak, Oliwia and Jorunn M. {Andersen} and Bj{\o}rndal, Lars and Riegler, Michael and Mette Haug {Stensen}}
}
@misc {27332,
	title = {Big data is not always better {\textendash} prediction of live birth using machine learning on time-lapse videos of human embryos},
	year = {2020},
	publisher = {ESHRE},
	address = {ESHRE virtual 36th Annual Meeting},
	author = {Hicks, Steven and Trine B. {Haugen} and Iliceto, Mario and Hugo Lewi {Hammer} and Jorunn M. {Andersen} and Witczak, Oliwia and Riegler, Michael and Stensen, Mette}
}
@article {26926,
	title = {Machine Learning-Based Analysis of Sperm Videos and Participant Data for Male Fertility Prediction},
	journal = {Nature Scientific Reports },
	volume = {9},
	number = {16770},
	year = {2019},
	publisher = {Springer Nature},
	abstract = {Methods for automatic analysis of clinical data are usually targeted towards a specific modality and do not make use of all relevant data available. In the field of male human reproduction, clinical and biological dataare not used to its fullest potential. Manual evaluation of a semen sample using a microscope is time-consuming and requires extensive training. Furthermore, the validity of manual semen analysis has been questioned due to limited reproducibility, and often high inter-personnel variation. The existing computer-aided sperm analyzer systems are not recommended for routine clinical use due to methodological challenges caused by the consistency of the semen sample. Thus, there is a need for an improved methodology.We use modern and classical machine learning techniques together with a dataset consisting of\&nbsp;85\&nbsp;videos of human semen samples and related participant data to automatically predict sperm motility. Used techniques include simple linear regression and more sophisticated methods using convolutional neural networks. Our results indicate that sperm motility prediction based on deep learning using sperm motility videos is rapid to perform and consistent. The algorithms performed worse when participant data was added. In conclusion, machine learning-based automatic analysis may become a valuable tool in male infertility investigation and research.},
	author = {Hicks, Steven and Jorunn M. {Andersen} and Witczak, Oliwia and Thambawita, Vajira and Halvorsen, P{\r a}l and Hugo Lewi {Hammer} and Trine B. {Haugen} and Riegler, Michael}
}
@inproceedings {26931,
	title = {Medico Multimedia Task at MediaEval 2019},
	journal = {MediaEval},
	year = {2019},
	publisher = {CEUR Workshop Proceedings},
	abstract = {The Medico: Multimedia for Medicine Task\&nbsp;is running for the third time as part of MediaEval 2019. This year, we have changed the task from anomaly detection in images of the gastrointestinal tract to focus on the automatic prediction of human semen quality based on videos. The purpose of this task is to aid in the assessment of male reproductive health by providing a quick and consisted method of analyzing human semen. In this paper, we describe the task in detail, give a brief description of the provided dataset, and discuss the evaluation process and the metrics used to rank the submissions of the participants.},
	author = {Hicks, Steven and Halvorsen, P{\r a}l and Trine B. {Haugen} and Jorunn M. {Andersen} and Witczak, Oliwia and Pogorelov, Konstantin and Hugo Lewi {Hammer} and Dang-Nguyen, Duc-Tien and Lux, Mathias and Riegler, Michael}
}
@inproceedings {26929,
	title = {Predicting Sperm Motility and Morphology using Deep Learning and Handcrafted Features},
	journal = {MediaEval},
	year = {2019},
	publisher = {ceur ws org},
	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.},
	author = {Hicks, Steven and Halvorsen, P{\r a}l and Trine B. {Haugen} and Jorunn M. {Andersen} and Witczak, Oliwia and Pogorelov, Konstantin and Hugo Lewi {Hammer} and Dang-Nguyen, Duc-Tien and Lux, Mathias and Riegler, Michael}
}
