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Search results for biblio_type:Talk, keynote
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2022
V. Thambawita, S. Hicks, A. Storås, O. Witczak, J. M. Andersen, H. L. Hammer, P. Halvorsen, M. Riegler and H. B. Trine. "Real-time deep learning based multi object tracking of spermatozoa in fresh samples." Human Reproduction 37, no. Supplement_1, July 2022, deac107.104 (2022).PDF icon deac107.104.pdf (72.6 KB)
2021
T. B. Haugen, S. Hicks, O. Witczak, J. M. Andersen, L. Bjørndal, M. Riegler and M. H. Stensen. Assessment of sperm motility according to WHO classification using convolutional neural networks. ESHRE: ESHRE, 2021.
2020
S. Hicks, T. B. Haugen, M. Iliceto, H. L. Hammer, J. M. Andersen, O. Witczak, M. Riegler and M. Stensen. Big data is not always better – prediction of live birth using machine learning on time-lapse videos of human embryos. ESHRE virtual 36th Annual Meeting: ESHRE, 2020.
2019
S. Hicks, J. M. Andersen, O. Witczak, V. Thambawita, P. Halvorsen, H. L. Hammer, T. B. Haugen and M. Riegler. "Machine Learning-Based Analysis of Sperm Videos and Participant Data for Male Fertility Prediction." Nature Scientific Reports 9, no. 1 (2019).
S. Hicks, P. Halvorsen, T. B. Haugen, J. M. Andersen, O. Witczak, K. Pogorelov, H. L. Hammer, D. Dang-Nguyen, M. Lux and M. Riegler. Medico Multimedia Task at MediaEval 2019 In MediaEval. CEUR Workshop Proceedings, 2019.
S. Hicks, P. Halvorsen, T. B. Haugen, J. M. Andersen, O. Witczak, K. Pogorelov, H. L. Hammer, D. Dang-Nguyen, M. Lux and M. Riegler. Predicting Sperm Motility and Morphology using Deep Learning and Handcrafted Features In MediaEval. ceur ws org, 2019.