Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions

Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies h...

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Detalles Bibliográficos
Autores principales: Nan, Yang, Ser, Javier Del, Walsh, Simon, Schönlieb, Carola, Roberts, Michael, Selby, Ian, Howard, Kit, Owen, John, Neville, Jon, Guiot, Julien, Ernst, Benoit, Pastor, Ana, Alberich-Bayarri, Angel, Menzel, Marion I., Walsh, Sean, Vos, Wim, Flerin, Nina, Charbonnier, Jean-Paul, van Rikxoort, Eva, Chatterjee, Avishek, Woodruff, Henry, Lambin, Philippe, Cerdá-Alberich, Leonor, Martí-Bonmatí, Luis, Herrera, Francisco, Yang, Guang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8878813/
https://www.ncbi.nlm.nih.gov/pubmed/35664012
http://dx.doi.org/10.1016/j.inffus.2022.01.001

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8878813/
https://www.ncbi.nlm.nih.gov/pubmed/35664012
http://dx.doi.org/10.1016/j.inffus.2022.01.001

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