Baecker, Lea
17  Ergebnisse:
Personensuche X
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2

Using normative modelling to detect disease progression in ..:

Pinaya, Walter H L ; Scarpazza, Cristina ; Garcia-Dias, Rafael...
info:eu-repo/semantics/altIdentifier/doi/10.1038/s41598-021-95098-0.  , 2021
 
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3

Using normative modelling to detect disease progression in ..:

Lopez Pinaya, Walter ; Scarpazza, Cristina ; Garcia Dias, Rafael...
Lopez Pinaya , W , Scarpazza , C , Garcia Dias , R , Vieira , S , Baecker , L , Da Costa , P F , Redolfi , A , Frisoni , G , Pievani , M , Calhoun , V , Sato , J & Mechelli , A 2021 , ' Using normative modelling to detect disease progression in mild cognitive impairment and Alzheimer's disease in a cross-sectional multi-cohort study ' , Scientific Reports ..  , 2021
 
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4

Machine learning for brain age prediction: Introduction to ..:

Baecker, Lea ; Garcia-dias, Rafael ; Vieira, Sandra..
https://kclpure.kcl.ac.uk/portal/en/publications/70505f6d-6804-44d0-b6b4-29c0249203f6.  , 2021
 
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5

Brain age prediction: A comparison between machine learning..:

Baecker, Lea ; Dafflon, Jessica ; Da Costa, Pedro F...
https://kclpure.kcl.ac.uk/portal/en/publications/59a7ce75-eb71-476f-969d-f137e12047e3.  , 2021
 
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6

Machine learning for brain age prediction: Introduction to ..:

Baecker, Lea ; Garcia-dias, Rafael ; Vieira, Sandra..
Baecker , L , Garcia-dias , R , Vieira , S , Scarpazza , C & Mechelli , A 2021 , ' Machine learning for brain age prediction: Introduction to methods and clinical applications ' , EBioMedicine , vol. 72 , 103600 , pp. 103600 . https://doi.org/10.1016/j.ebiom.2021.103600.  , 2021
 
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7

Brain age prediction: A comparison between machine learning..:

Baecker, Lea ; Dafflon, Jessica ; Da Costa, Pedro F...
Baecker , L , Dafflon , J , Da Costa , P F , Garcia Dias , R , Vieira , S , Scarpazza , C , Calhoun , V D , Sato , J R , Mechelli , A & Pinaya , W H L 2021 , ' Brain age prediction: A comparison between machine learning models using region- and voxel-based morphometric data ' , Human Brain Mapping , vol. 42 , no. 8 , pp. 2332-2346 . https://doi.org/10.1002/hbm.25368.  , 2021
 
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8

Neuroharmony: A new tool for harmonizing volumetric MRI dat..:

Garcia Dias, Rafael ; Scarpazza, Cristina ; Baecker, Lea...
Garcia Dias , R , Scarpazza , C , Baecker , L , Mendes Vieira , S , Lopez Pinaya , W , Corvin , A , Redolf , A , Nelson , B , Crespo-Facorro , B , McDonald , C , Tordesillas-Gutiérrez , D , Cannon , D , Mothersill , D , Hernaus , D , Morris , D , Setien-Suero , E , Donohoe , G , Frisoniq , G , Tronchin , G , Sato , J , Marcelis , M , Kempton , M , van Haren , N E M , Gruber , O , McGorry , P , Amminger , P , McGuire , P , Gong , Q , Kahnz , R S , Ayesa-Arriola , R , van Amelsvoort , T , Ortiz-Garcia de la Foz , V , Calhoun , V , Cahn , W & Mechelli , A 2020 , ' Neuroharmony : A new tool for harmonizing volumetric MRI data from unseen scanners ' , NeuroImage , vol. 220 , 171127 . https://doi.org/10.1016/j.neuroimage.2020.117127.  , 2020
 
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9

Neuroharmony: A new tool for harmonizing volumetric MRI dat..:

Garcia Dias, Rafael ; Scarpazza, Cristina ; Baecker, Lea...
https://kclpure.kcl.ac.uk/portal/en/publications/ff064e40-a8ad-430e-a1e7-1a9b79c3722d.  , 2020
 
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11

Neuroharmony:A new tool for harmonizing volumetric MRI data..:

Garcia-Dias, Rafael ; Scarpazza, Cristina ; Baecker, Lea...
Garcia-Dias , R , Scarpazza , C , Baecker , L , Vieira , S , Pinaya , W H L , Corvin , A , Redolfi , A , Nelson , B , Crespo-Facorro , B , McDonald , C , Tordesillas-Gutierrez , D , Cannon , D , Mothersill , D , Hernaus , D , Morris , D , Setien-Suero , E , Donohoe , G , Frisoni , G , Tronchin , G , Sato , J , Marcelis , M , Kempton , M , van Haren , N E M , Gruber , O , McGorry , P , Amminger , P , McGuire , P , Gong , Q , Kahn , R S , Ayesa-Arriola , R , van Amelsvoort , T , de la Foz , V O-G , Calhoun , V , Cahn , W & Mechelli , A 2020 , ' Neuroharmony : A new tool for harmonizing volumetric MRI data from unseen scanners ' , Neuroimage , vol. 220 , 117127 . https://doi.org/10.1016/j.neuroimage.2020.117127.  , 2020
 
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