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Filters: 1 is biblio_type:PhD Thesis and Author is Trine B. Haugen  [Reset Search]
2022
S. Hicks, V. Thambawita, A. Storås, T. B. Haugen, H. L. Hammer, P. Halvorsen, M. Riegler and M. H. Stensen. "Automatic Tracking of the ICSI procedure using Deep Learning." Human Reproduction 37, no. Supplement_1 (2022).
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
V. Thambawita, P. Halvorsen, H. L. Hammer, M. Riegler and T. B. Haugen. Extracting temporal features into a spatial domain using autoencoders for sperm video analysis In MediaEval 2019. CEUR Workshop Proceedings, 2019.PDF icon medico_2019_paper_2_ae_based_method_vajira.pdf (1.6 MB)
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.
V. Thambawita, P. Halvorsen, H. L. Hammer, M. Riegler and T. B. Haugen. Stacked dense optical flows and dropout layers to predict sperm motility and morphology In MediaEval 2019, 27-29 October 2019, Sophia Antipolis, France., 2019.PDF icon medico_2019_vajira_1.pdf (1.43 MB)
S. Hicks, T. B. Haugen, P. Halvorsen and M. Riegler. Using Deep Learning to Predict Motility and Morphology of Human Sperm In MediaEval 2019. CEUR Workshop Proceedings, 2019.