Machine Learning for Brain MRI Data Harmonisation: A Systematic Review
Background: Magnetic Resonance Imaging (MRI) data collected from multiple centres can be heterogeneous due to factors such as the scanner used and the site location. To reduce this heterogeneity, the data needs to be harmonised. In recent years, machine learning (ML) has been used to solve different...
Main Authors: | Grace Wen, Vickie Shim, Samantha Jane Holdsworth, Justin Fernandez, Miao Qiao, Nikola Kasabov, Alan Wang |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2023-03-01
|
Series: | Bioengineering |
Subjects: | |
Online Access: | https://www.mdpi.com/2306-5354/10/4/397 |
Similar Items
-
The perfect diagnostic imaging machine and what it means for quantitative MRI reproducibility
by: Matt G. Hall, et al.
Published: (2023-12-01) -
Harmonisation of Pharmacopoeial Requirements for Identification of Closely Related Species in Herbal Medicinal Products
by: O. V. Evdokimova, et al.
Published: (2022-07-01) -
Internal Market 3.0: The Old “New Approach” for Harmonising AI Regulation
by: Sybe de Vries, et al.
Published: (2023-11-01) -
Ways of Harmonising Polish Competition Law with the Competition Law of the EU
by: Krystyna Kowalik-Bańczyk
Published: (2014-07-01) -
'The Fear of Insignificance': New Perspectives on Harmonising Police Cooperation in Europe and Australia
by: Saskia Hufnagel
Published: (2010-06-01)