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» Subspace Models for Functional MRI Data Analysis
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ICASSP
2008
IEEE
15 years 4 months ago
Controlling the false discovery rate in modeling brain functional connectivity
Graphical models of brain functional connectivity have matured from con rming a priori hypotheses to an exploratory tool for discovering unknown connectivity. However, exploratory...
Junning Li, Z. Jane Wang, Martin J. McKeown
MICCAI
2004
Springer
15 years 10 months ago
Segmentation of 3D Probability Density Fields by Surface Evolution: Application to Diffusion MRI
We propose an original approach for the segmentation of three-dimensional fields of probability density functions. This presents a wide range of applications in medical images proc...
Christophe Lenglet, Mikaël Rousson, Rachid De...
MICCAI
2005
Springer
15 years 10 months ago
MRI Tissue Classification with Neighborhood Statistics: A Nonparametric, Entropy-Minimizing Approach
We introduce a novel approach for magnetic resonance image (MRI) brain tissue classification by learning image neighborhood statistics from noisy input data using nonparametric den...
Tolga Tasdizen, Suyash P. Awate, Ross T. Whitaker,...
CVPR
2011
IEEE
14 years 1 months ago
Generalized Group Sparse Classifiers with Application in fMRI Brain Decoding
The perplexing effects of noise and high feature dimensionality greatly complicate functional magnetic resonance imaging (fMRI) classification. In this paper, we present a novel f...
Bernard Ng, Rafeef Abugharbieh
NN
2000
Springer
159views Neural Networks» more  NN 2000»
14 years 9 months ago
Independent component analysis for noisy data -- MEG data analysis
ICA (independent component analysis) is a new, simple and powerful idea for analyzing multi-variant data. One of the successful applications is neurobiological data analysis such ...
Shiro Ikeda, Keisuke Toyama