Publicaciones en las que colabora con DARYA CHYZHYK (30)

2017

  1. Brain white matter lesion segmentation with 2D/3D CNN

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

  2. Resting State Effective Connectivity Allows Auditory Hallucination Discrimination

    International Journal of Neural Systems, Vol. 27, Núm. 5

  3. White matter tract integrity in Alzheimer's disease vs. Late Onset Bipolar Disorder and its correlation with systemic inflammation and oxidative stress biomarkers

    Frontiers in Aging Neuroscience, Vol. 9, Núm. JUN

2015

  1. An active learning approach for stroke lesion segmentation on multimodal MRI data

    Neurocomputing, Vol. 150, Núm. Part A, pp. 26-36

  2. Classification of schizophrenia patients on lattice computing resting-state fMRI features

    Neurocomputing, Vol. 151, Núm. P1, pp. 151-160

  3. Computer aided diagnosis of schizophrenia on resting state fMRI data by ensembles of ELM

    Neural Networks, Vol. 68, pp. 23-33

  4. Discrimination between Alzheimer's disease and late onset bipolar disorder using multivariate analysis

    Frontiers in Aging Neuroscience, Vol. 7, Núm. DEC

  5. Discrimination of schizophrenia auditory hallucinators by machine learning of resting-state functional MRI

    International Journal of Neural Systems, Vol. 25, Núm. 3

  6. Some results on dynamic causal modeling of auditory hallucinations

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

2014

  1. Computer aided diagnosis of schizophrenia based on local-activity measures of resting-state fMRI

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

  2. Evolutionary ELM wrapper feature selection for Alzheimer's disease CAD on anatomical brain MRI

    Neurocomputing, Vol. 128, pp. 73-80

  3. Findings in resting-state fMRI by differences from K-means clustering

    Studies in Health Technology and Informatics