Výsledky vyhledávání - "Leming, Matthew"

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Adversarial confound regression and uncertainty measurements to classify heterogeneous clinical MRI in Mass General Brigham

Autor: Leming, Matthew, Das, Sudeshna, Im, Hyungsoon

Automated disease detection in neuroimaging holds promise to improve the diagnostic ability of radiologists, but routinely collected clinical data frequently contains technical and demographic confounding factors that cause data to both differ betwee

Externí odkaz: http://arxiv.org/abs/2205.02885

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Single-participant structural connectivity matrices lead to greater accuracy in classification of participants than function in autism in MRI

Autor: Leming, Matthew, Baron-Cohen, Simon, Suckling, John

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 the technique by inputting the connectivity matrices into a convoluti

Externí odkaz: http://arxiv.org/abs/2005.08035

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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

Autor: Leming, Matthew, Suckling, John

Classification of whole-brain functional connectivity MRI data with convolutional neural networks (CNNs) has shown promise, but the complexity of these models impedes understanding of which aspects of brain activity contribute to classification. Whil

Externí odkaz: http://arxiv.org/abs/2002.10936

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Ensemble Deep Learning on Large, Mixed-Site fMRI Datasets in Autism and Other Tasks

Autor: Leming, Matthew, Górriz, Juan Manuel, Suckling, John

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 problem"). In this paper, we train a convolutional neural network (CNN)

Externí odkaz: http://arxiv.org/abs/2002.07874

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