Zobrazeno 1 - 10
of 83
pro vyhledávání: '"Kragh, Mikkel"'
Autor:
Lassen, Jacob Theilgaard, Kragh, Mikkel Fly, Rimestad, Jens, Johansen, Martin Nygård, Berntsen, Jørgen
This work describes the development and validation of a fully automated deep learning model, iDAScore v2.0, for the evaluation of embryos incubated for 2, 3, and 5 or more days. The model is trained and evaluated on an extensive and diverse dataset i
Externí odkaz:
http://arxiv.org/abs/2210.02120
Assessing and selecting the most viable embryos for transfer is an essential part of in vitro fertilization (IVF). In recent years, several approaches have been made to improve and automate the procedure using artificial intelligence (AI) and deep le
Externí odkaz:
http://arxiv.org/abs/2103.07262
It is hard to create consistent ground truth data for interest points in natural images, since interest points are hard to define clearly and consistently for a human annotator. This makes interest point detectors non-trivial to build. In this work,
Externí odkaz:
http://arxiv.org/abs/1907.04011
Autor:
Kato, Keiichi, Ueno, Satoshi, Berntsen, Jørgen, Kragh, Mikkel Fly, Okimura, Tadashi, Kuroda, Tomoko
Publikováno v:
In Reproductive BioMedicine Online February 2023 46(2):274-281
In recent years, the drive of the Industry 4.0 initiative has enriched industrial and scientific approaches to build self-driving cars or smart factories. Agricultural applications benefit from both advances, as they are in reality mobile driving fac
Externí odkaz:
http://arxiv.org/abs/1805.08595
Autor:
Kragh, Mikkel Fly, Christiansen, Peter, Laursen, Morten Stigaard, Larsen, Morten, Steen, Kim Arild, Green, Ole, Karstoft, Henrik, Jørgensen, Rasmus Nyholm
Publikováno v:
Sensors 2017, 17(11), 2579
In this paper, we present a novel multi-modal dataset for obstacle detection in agriculture. The dataset comprises approximately 2 hours of raw sensor data from a tractor-mounted sensor system in a grass mowing scenario in Denmark, October 2016. Sens
Externí odkaz:
http://arxiv.org/abs/1709.03526
Autor:
Kragh, Mikkel, Underwood, James
Reliable obstacle detection and classification in rough and unstructured terrain such as agricultural fields or orchards remains a challenging problem. These environments involve large variations in both geometry and appearance, challenging perceptio
Externí odkaz:
http://arxiv.org/abs/1706.02908
Development and validation of deep learning based embryo selection across multiple days of transfer.
Autor:
Theilgaard Lassen, Jacob1 (AUTHOR) jtlassen@vitrolife.com, Fly Kragh, Mikkel1 (AUTHOR), Rimestad, Jens1 (AUTHOR), Nygård Johansen, Martin1 (AUTHOR), Berntsen, Jørgen1 (AUTHOR)
Publikováno v:
Scientific Reports. 3/14/2023, Vol. 13 Issue 1, p1-9. 9p.
Publikováno v:
In Computers in Biology and Medicine December 2019 115