3 papers
q-bio.QM2020
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…
q-bio.NC2020
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…
q-bio.QM2020
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…