Using graph convolutional network to characterize individuals with major depressive disorder across multiple imaging sites

Autor: Kun Qin, Du Lei, Walter H.L. Pinaya, Nanfang Pan, Wenbin Li, Ziyu Zhu, John A. Sweeney, Andrea Mechelli, Qiyong Gong

Publikováno v: EBioMedicine, Vol 78, Iss , Pp 103977- (2022)

Summary: Background: Establishing objective and quantitative neuroimaging biomarkers at individual level can assist in early and accurate diagnosis of major depressive disorder (MDD). However, most previous studies using machine learning to identify

Externí odkaz: https://doaj.org/article/044beed78a8249f9ba137c397118500b

Cross Attention Transformers for Multi-modal Unsupervised Whole-Body PET Anomaly Detection

Autor: Ashay Patel, Petru-Danial Tudiosu, Walter H.L. Pinaya, Gary Cook, Vicky Goh, Sebastien Ourselin, M. Jorge Cardoso

Publikováno v: Machine Learning for Biomedical Imaging. 2:172-201

Cancer is a highly heterogeneous condition that can occur almost anywhere in the human body. 18F-fluorodeoxyglucose is an imaging modality commonly used to detect cancer due to its high sensitivity and clear visualisation of the pattern of metabolic

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::edbec600272676e77a55263982291be8
https://doi.org/10.59275/j.melba.2023-18c1

Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications

Autor: Vieira, Sandra, Pinaya, Walter H.L., Mechelli, Andrea

Publikováno v: In Neuroscience and Biobehavioral Reviews March 2017 74 Part A:58-75

Unsupervised brain imaging 3D anomaly detection and segmentation with transformers

Autor: Walter H.L. Pinaya, Petru-Daniel Tudosiu, Robert Gray, Geraint Rees, Parashkev Nachev, Sebastien Ourselin, M. Jorge Cardoso

Publikováno v: Pinaya, W H L, Tudosiu, P-D, Gray, R, Rees, G, Nachev, P, Ourselin, S & Cardoso, M J 2022, ' Unsupervised Brain Imaging 3D Anomaly Detection and Segmentation with Transformers ', Medical Image Analysis, vol. 79, 102475 . https://doi.org/10.1016/j.media.2022.102475

Pathological brain appearances may be so heterogeneous as to be intelligible only as anomalies, defined by their deviation from normality rather than any specific set of pathological features. Amongst the hardest tasks in medical imaging, detecting s

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d3775e148e17f9ba6085de3b19058632
https://pubmed.ncbi.nlm.nih.gov/35598520

Using machine learning and structural neuroimaging to detect first episode psychosis: Reconsidering the evidence

Autor: Vieira, Sandra, Gong, Qi Yong, Pinaya, Walter H.L., Scarpazza, Cristina, Tognin, Stefania, Crespo Facorro, Benedicto, Mechelli, Andrea

Despite the high level of interest in the use of machine learning (ML) and neuroimaging to detect psychosis at the individual level, the reliability of the findings is unclear due to potential methodological issues that may have inflated the existing

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______3272::26212800cbfc23f5de34621a10c38559