74 citations · 119 across the 5 of their papers we have counts for
5 papers
Deep Learning in current Neuroimaging: a multivariate approach with power and type I error control but arguable generalization ability
Carmen Jiménez-Mesa, Javier Ramírez, John Suckling +5
Discriminative analysis in neuroimaging by means of deep/machine learning techniques is usually tested with validation techniques, whereas the associated statistical significance r…
A connection between the pattern classification problem and the General Linear Model for statistical inference
Juan Manuel Gorriz, SIPBA group, John Suckling
A connection between the General Linear Model (GLM) in combination with classical statistical inference and the machine learning (MLE)-based inference is described in this paper. F…
Single-participant structural connectivity matrices lead to greater accuracy in classification of participants than function in autism in MRI
Matthew Leming, Simon Baron-Cohen, John Suckling
In this work, we introduce a technique of deriving symmetric connectivity matrices from regional histograms of grey-matter volume estimated from T1-weighted MRIs. We then validated…
Stochastic encoding of graphs in deep learning allows for complex analysis of gender classification in resting-state and task functional brain networks from the UK Biobank
Matthew Leming, John Suckling
Classification of whole-brain functional connectivity MRI data with convolutional neural networks (CNNs) has shown promise, but the complexity of these models impedes understanding…
Ensemble Deep Learning on Large, Mixed-Site fMRI Datasets in Autism and Other Tasks
Matthew Leming, Juan Manuel Górriz, John Suckling
Deep learning models for MRI classification face two recurring problems: they are typically limited by low sample size, and are abstracted by their own complexity (the "black box p…