Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Martin Kiik"'
Autor:
Leho Tedersoo, Jaan Sepping, Alexey S. Morgunov, Martin Kiik, Kristiina Esop, Raul Rosenvald, Kate Hardwick, Elinor Breman, Rachel Purdon, Ben Groom, Frank Venmans, E. Toby Kiers, Alexandre Antonelli
Publikováno v:
Plants, People, Planet, Vol 6, Iss 1, Pp 18-28 (2024)
Societal Impact Statement Humankind is facing both climate and biodiversity crises. This article proposes the foundations of a scheme that offers tradable credits for combined aboveground and soil carbon and biodiversity. Multidiversity—as estimate
Externí odkaz:
https://doaj.org/article/f210a52e823c4766a71df1633f1f60e8
Autor:
Wenjuan Wang, Martin Kiik, Niels Peek, Vasa Curcin, Iain J Marshall, Anthony G Rudd, Yanzhong Wang, Abdel Douiri, Charles D Wolfe, Benjamin Bray
Publikováno v:
PLoS ONE, Vol 15, Iss 6, p e0234722 (2020)
Background and purposeMachine learning (ML) has attracted much attention with the hope that it could make use of large, routinely collected datasets and deliver accurate personalised prognosis. The aim of this systematic review is to identify and cri
Externí odkaz:
https://doaj.org/article/d79ebca6ab2441438b082027426c85f6
Autor:
Asif Mazumder, James H. Cole, Thomas C. Booth, Jeremy Lynch, Matthew Townend, Sina Kafiabadi, Matthew Benger, Martin Kiik, Juveria Siddiqui, Aisha Al Busaidi, Sebastian Ourselin, Emily Guilhem, Antanas Montvila, David A. Wood, Gareth J. Barker, Naveen Gadapa
Publikováno v:
Wood, D A, Kafiabadi, S, Al Busaidi, A, Guilhem, E L, Lynch, J, Townend, M K, Montvila, A, Kiik, M, Siddiqui, J, Gadapa, N, Benger, M D, Mazumder, A, Barker, G, Ourselin, S, Cole, J H & Booth, T 2022, ' Deep learning to automate the labelling of head MRI datasets for computer vision applications ', European Radiology, vol. 32, no. 1, pp. 725-736 . https://doi.org/10.1007/s00330-021-08132-0
European Radiology
European Radiology
Objectives The purpose of this study was to build a deep learning model to derive labels from neuroradiology reports and assign these to the corresponding examinations, overcoming a bottleneck to computer vision model development. Methods Reference-s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::19830b6d7678049b7feeee154d6ea10c
https://kclpure.kcl.ac.uk/ws/files/156367278/Deep_learning_to_automate_WOOD_Accepted14June2021_GOLD_VoR.pdf
https://kclpure.kcl.ac.uk/ws/files/156367278/Deep_learning_to_automate_WOOD_Accepted14June2021_GOLD_VoR.pdf
Autor:
Vasa Curcin, Iain J. Marshall, Charles D.A. Wolfe, Benjamin Bray, Wenjuan Wang, Anthony Rudd, Yanzhong Wang, Abdel Douiri, Niels Peek, Martin Kiik
Publikováno v:
Wang, W, Kiik, M, Peek, N, Curcin, V, Marshall, I J, Rudd, A G, Wang, Y, Douiri, A, Wolfe, C D & Bray, B 2020, ' A systematic review of machine learning models for predicting outcomes of stroke with structured data ', PLoS ONE, vol. 15, no. 6, pp. e0234722 . https://doi.org/10.1371/journal.pone.0234722
PLoS ONE
PLoS ONE, Vol 15, Iss 6, p e0234722 (2020)
PLoS ONE
PLoS ONE, Vol 15, Iss 6, p e0234722 (2020)
Background: Machine learning (ML) attracts many attentions with the hope that it could make use of large, routinely collected datasets and deliver accurate personalised prognosis. The aim of this systematic review is to identify and critically apprai