Learning patterns of the ageing brain in MRI using deep convolutional networks
Both normal ageing and neurodegenerative diseases cause morphological changes to the brain. Age-related brain changes are subtle, nonlinear, and spatially and temporally heterogenous, both within a subject and across a population. Machine learning models are particularly suited to capture these patt...
Main Authors: | Dinsdale, NK, Bluemke, E, Smith, SM, Arya, Z, Vidaurre, D, Jenkinson, M, Namburete, AIL |
---|---|
Format: | Journal article |
Language: | English |
Published: |
Elsevier
2020
|
Similar Items
-
Deep learning-based unlearning of dataset bias for MRI harmonisation and confound removal.
by: Dinsdale, NK, et al.
Published: (2020) -
Unlearning scanner bias for MRI harmonisation
by: Dinsdale, NK, et al.
Published: (2020) -
Prototype learning for explainable brain age prediction
by: Hesse, LS, et al.
Published: (2024) -
Unlearning scanner bias for MRI harmonisation in medical image segmentation
by: Dinsdale, NK, et al.
Published: (2020) -
Challenges for machine learning in clinical translation of big data imaging studies
by: Dinsdale, NK, et al.
Published: (2022)