High-speed camera system for efficient monitoring of invasive plant species along roadways [version 2; peer review: 1 approved, 2 approved with reservations]

Autor: Mikkel Fly Kragh, Anders Krogh Mortensen, Søren Kelstrup Skovsen, Peter Hviid Christiansen, Mads Dyrmann

Publikováno v: F1000Research, Vol 13 (2024)

Invasive plant species pose ecological threats to native ecosystems, particularly in areas adjacent to roadways, considering that roadways represent lengthy corridors through which invasive species can propagate. Traditional manual survey methods for

Externí odkaz: https://doaj.org/article/6ea6a4cb2e914a08b5963aa10c7582af

Development and validation of deep learning based embryo selection across multiple days of transfer

Autor: Jacob Theilgaard Lassen, Mikkel Fly Kragh, Jens Rimestad, Martin Nygård Johansen, Jørgen Berntsen

Publikováno v: Scientific Reports, Vol 13, Iss 1, Pp 1-9 (2023)

Abstract This work describes the development and validation of a fully automated deep learning model, iDAScore v2.0, for the evaluation of human embryos incubated for 2, 3, and 5 or more days. We trained and evaluated the model on an extensive and di

Externí odkaz: https://doaj.org/article/70e12fafb43940a3aca04a4f6ca57aa8

FieldSAFE: Dataset for Obstacle Detection in Agriculture

Autor: Mikkel Fly Kragh, Peter Christiansen, Morten Stigaard Laursen, Morten Larsen, Kim Arild Steen, Ole Green, Henrik Karstoft, Rasmus Nyholm Jørgensen

Publikováno v: Sensors, Vol 17, Iss 11, p 2579 (2017)

In this paper, we present a multi-modal dataset for obstacle detection in agriculture. The dataset comprises approximately 2 h of raw sensor data from a tractor-mounted sensor system in a grass mowing scenario in Denmark, October 2016. Sensing modali

Externí odkaz: https://doaj.org/article/84cd2018be924b51b88aeda15006c944

Embryo selection with artificial intelligence: how to evaluate and compare methods?

Autor: Mikkel Fly Kragh, Henrik Karstoft

Publikováno v: Kragh, M F & Karstoft, H 2021, ' Embryo selection with artificial intelligence: how to evaluate and compare methods? ', Journal of Assisted Reproduction and Genetics, vol. 38, pp. 1675-1689 . https://doi.org/10.1007/s10815-021-02254-6
Journal of Assisted Reproduction and Genetics

Embryo selection within in vitro fertilization (IVF) is the process of evaluating qualities of fertilized oocytes (embryos) and selecting the best embryo(s) available within a patient cohort for subsequent transfer or cryopreservation. In recent year

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::21228ad3694cd3af8d67debcf1674aec
https://doi.org/10.1007/s10815-021-02254-6

Predicting Embryo Viability Based on Self-Supervised Alignment of Time-Lapse Videos

Autor: Jørgen Berntsen, Jacob Theilgaard Lassen, Jens Rimestad, Henrik Karstoft, Mikkel Fly Kragh

Publikováno v: Kragh, M F, Rimestad, J, Lassen, J T, Berntsen, J & Karstoft, H 2022, ' Predicting embryo viability based on self-supervised alignment of time-lapse videos ', IEEE Transactions on Medical Imaging, vol. 41, no. 2, pp. 465-475 . https://doi.org/10.1109/TMI.2021.3116986

With self-supervised learning, both labeled and unlabeled data can be used for representation learning and model pretraining. This is particularly relevant when automating the selection of a patient's fertilized eggs (embryos) during a fertility trea

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b0146de3fc9a84d0cbfaf646db6e8952
https://pubmed.ncbi.nlm.nih.gov/34596537