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Walter Hugo Lopez Pinaya
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Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications
S Vieira, WHL Pinaya, A Mechelli
Neuroscience & Biobehavioral Reviews 74, 58-75, 2017
6442017
Using deep belief network modelling to characterize differences in brain morphometry in schizophrenia
WHL Pinaya, A Gadelha, OM Doyle, C Noto, A Zugman, Q Cordeiro, ...
Scientific reports 6 (1), 38897, 2016
1832016
Brain imaging generation with latent diffusion models
WHL Pinaya, PD Tudosiu, J Dafflon, PF Da Costa, V Fernandez, ...
MICCAI Workshop on Deep Generative Models, 117-126, 2022
1772022
Autoencoders
WHL Pinaya, S Vieira, R Garcia-Dias, A Mechelli
Machine learning, 193-208, 2020
1762020
Using deep autoencoders to identify abnormal brain structural patterns in neuropsychiatric disorders: A large‐scale multi‐sample study
WHL Pinaya, A Mechelli, JR Sato
Human brain mapping 40 (3), 944-954, 2019
1232019
Using machine learning and structural neuroimaging to detect first episode psychosis: reconsidering the evidence
S Vieira, Q Gong, WHL Pinaya, C Scarpazza, S Tognin, B Crespo-Facorro, ...
Schizophrenia bulletin 46 (1), 17-26, 2020
942020
Convolutional neural networks
WHL Pinaya, S Vieira, R Garcia-Dias, A Mechelli
Machine learning, 173-191, 2020
942020
Fast unsupervised brain anomaly detection and segmentation with diffusion models
WHL Pinaya, MS Graham, R Gray, PF Da Costa, PD Tudosiu, P Wright, ...
International Conference on Medical Image Computing and Computer-Assisted …, 2022
912022
Unsupervised brain imaging 3D anomaly detection and segmentation with transformers
WHL Pinaya, PD Tudosiu, R Gray, G Rees, P Nachev, S Ourselin, ...
Medical Image Analysis 79, 102475, 2022
842022
Brain age prediction: A comparison between machine learning models using region‐and voxel‐based morphometric data
L Baecker, J Dafflon, PF Da Costa, R Garcia‐Dias, S Vieira, C Scarpazza, ...
Human brain mapping 42 (8), 2332-2346, 2021
792021
Detecting schizophrenia at the level of the individual: relative diagnostic value of whole-brain images, connectome-wide functional connectivity and graph-based metrics
D Lei, WHL Pinaya, T Van Amelsvoort, M Marcelis, G Donohoe, ...
Psychological medicine 50 (11), 1852-1861, 2020
722020
Integrating machining learning and multimodal neuroimaging to detect schizophrenia at the level of the individual
D Lei, WHL Pinaya, J Young, T Van Amelsvoort, M Marcelis, G Donohoe, ...
Human brain mapping 41 (5), 1119-1135, 2020
712020
Introduction to machine learning
S Vieira, WHL Pinaya, A Mechelli
Machine learning, 1-20, 2020
642020
Neuroharmony: A new tool for harmonizing volumetric MRI data from unseen scanners
R Garcia-Dias, C Scarpazza, L Baecker, S Vieira, WHL Pinaya, A Corvin, ...
Neuroimage 220, 117127, 2020
602020
Unsupervised brain anomaly detection and segmentation with transformers
WHL Pinaya, PD Tudosiu, R Gray, G Rees, P Nachev, S Ourselin, ...
arXiv preprint arXiv:2102.11650, 2021
562021
Clustering analysis
R Garcia-Dias, S Vieira, WHL Pinaya, A Mechelli
machine learning, 227-247, 2020
562020
An automated machine learning approach to predict brain age from cortical anatomical measures
J Dafflon, WHL Pinaya, F Turkheimer, JH Cole, R Leech, MA Harris, ...
Human brain mapping 41 (13), 3555-3566, 2020
482020
Denoising diffusion models for out-of-distribution detection
MS Graham, WHL Pinaya, PD Tudosiu, P Nachev, S Ourselin, J Cardoso
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023
462023
Using normative modelling to detect disease progression in mild cognitive impairment and Alzheimer’s disease in a cross-sectional multi-cohort study
WHL Pinaya, C Scarpazza, R Garcia-Dias, S Vieira, L Baecker, ...
Scientific reports 11 (1), 15746, 2021
452021
Using graph convolutional network to characterize individuals with major depressive disorder across multiple imaging sites
K Qin, D Lei, WHL Pinaya, N Pan, W Li, Z Zhu, JA Sweeney, A Mechelli, ...
EBioMedicine 78, 2022
422022
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