| 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 | 122 | 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 | 83 | 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 | 23 | 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 | 21 | 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 | 20 | 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 | 15 | 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 | 7 | 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 | 1 | 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 | | 2017 |
| New approaches for fMRI functional connectivity analysis based on Dynamic Time Warping and machine learning RJ Meszlényi | | 2017 |
Zoltán VidnyánszkyProfessor of NeuroscienceVerified email at ttk.hu
