Regina Deák-Meszlényi
Regina Deák-Meszlényi
Deep learning for computer vision, Continental
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Resting state fMRI functional connectivity-based classification using a convolutional neural network architecture
RJ Meszlényi, K Buza, Z Vidnyánszky
Frontiers in neuroinformatics 11, 61, 2017
Resting state fMRI functional connectivity analysis using dynamic time warping
RJ Meszlényi, P Hermann, K Buza, V Gál, Z Vidnyánszky
Frontiers in neuroscience 11, 75, 2017
Classification of fMRI data using dynamic time warping based functional connectivity analysis
R Meszlényi, L Peska, V Gál, Z Vidnyánszky, K Buza
2016 24th European signal processing conference (EUSIPCO), 245-249, 2016
Feature selection with a genetic algorithm for classification of brain imaging data
A Szenkovits, R Meszlényi, K Buza, N Gaskó, RI Lung, M Suciu
Advances in feature selection for data and pattern recognition, 185-202, 2018
Transfer learning improves resting-state functional connectivity pattern analysis using convolutional neural networks
P Vakli, RJ Deák-Meszlényi, P Hermann, Z Vidnyánszky
Gigascience 7 (12), giy130, 2018
Predicting body mass index from structural MRI brain images using a deep convolutional neural network
P Vakli, RJ Deák-Meszlényi, T Auer, Z Vidnyánszky
Frontiers in Neuroinformatics 14, 10, 2020
A model for classification based on the functional connectivity pattern dynamics of the brain
R Meszlényi, L Peska, V Gál, Z Vidnyánszky, K Buza
2016 Third European Network Intelligence Conference (ENIC), 203-208, 2016
Community structure detection for the functional connectivity networks of the brain
RI Lung, M Suciu, R Meszlényi, K Buza, N Gaskó
Parallel Problem Solving from Nature–PPSN XIV: 14th International Conference …, 2016
P242 Dynamic time warping distance based connectivity: A new method for resting-state FMRI functional connectivity analysis
R Meszlényi, L Peska, P Hermann, K Buza, V Gál, Z Vidnyánszky
Clinical Neurophysiology 128 (9), e256, 2017
New approaches for fMRI functional connectivity analysis based on Dynamic Time Warping and machine learning
RJ Meszlényi
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