Carlton Chu

According to our database1, Carlton Chu authored at least 17 papers between 2008 and 2020.

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Bibliography

2020
The influence of microsatellite polymorphisms in sex steroid receptor genes ESR1, ESR2 and AR on sex differences in brain structure.
NeuroImage, 2020

2018
Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy.
CoRR, 2018

2017
The Neuro Bureau ADHD-200 Preprocessed repository.
NeuroImage, 2017

2016
Face-selective regions differ in their ability to classify facial expressions.
NeuroImage, 2016

Automated analysis of retinal imaging using machine learning techniques for computer vision.
F1000Research, 2016

Applying machine learning to automated segmentation of head and neck tumour volumes and organs at risk on radiotherapy planning CT and MRI scans.
F1000Research, 2016

2013
PRoNTo: Pattern Recognition for Neuroimaging Toolbox.
Neuroinformatics, 2013

Effects of image contrast on functional MRI image registration.
NeuroImage, 2013

2012
Does feature selection improve classification accuracy? Impact of sample size and feature selection on classification using anatomical magnetic resonance images.
NeuroImage, 2012

2011
Kernel regression for fMRI pattern prediction.
NeuroImage, 2011

Utilizing temporal information in fMRI decoding: Classifier using kernel regression methods.
NeuroImage, 2011

Measuring the Consistency of Global Functional Connectivity Using Kernel Regression Methods.
Proceedings of the 2011 International Workshop on Pattern Recognition in NeuroImaging, 2011

2010
Predicting clinical scores from magnetic resonance scans in Alzheimer's disease.
NeuroImage, 2010

Quantitative prediction of subjective pain intensity from whole-brain fMRI data using Gaussian processes.
NeuroImage, 2010

2008
Interpreting scan data acquired from multiple scanners: A study with Alzheimer's disease.
NeuroImage, 2008

Bayesian decoding of brain images.
NeuroImage, 2008

Kernel methods for fMRI pattern prediction.
Proceedings of the International Joint Conference on Neural Networks, 2008


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