Analysis of Multidimensional Neural Activity Via CNN-UM. - Viktor Gál; Sonja Grün; Ronald Tetzlaff (Int. J. Neural Syst., 2003)

Receptive Field Atlas and Related CNN Models. - Viktor Gál; József Hámori; Tamás Roska; Dávid Bálya; Zsolt Borostyánköi; Mátyás Brendel; Károly Lotz; Laszlo Négyessy; László Orzó; István Petrás; Csaba Rekeczky; József Takács; et al. (Int. J. Bifurc. Chaos, 2004)

Research Paper: Semantic Classification of Diseases in Discharge Summaries Using a Context-aware Rule-based Classifier. - Illés Solt; Domonkos Tikk; Viktor Gál; Zsolt Tivadar Kardkovács (J. Am. Medical Informatics Assoc., 2009)

Neural mechanisms for voice recognition. - Attila Andics; James M. McQueen; Karl Magnus Petersson; Viktor Gál; Gábor Rudas; Zoltán Vidnyánszky (NeuroImage, 2010)

Multi-disciplinary Modality Classification for Medical Images. - Viktor Gál; Illés Solt; Tom Gedeon; Mike Nachtegael (CLEF (Notebook Papers/Labs/Workshop), 2011)

Multiple kernel learning based modality classification for medical images. - Viktor Gál; Etienne E. Kerre; Mike Nachtegael (CVPR Workshops, 2012)

Modality Classification for Medical Images Using Sparse Coded Affine-Invariant Descriptors. - Viktor Gál; Illés Solt; Etienne E. Kerre; Mike Nachtegael (PAKDD Workshops, 2012)

FMRI repetition suppression for voices is modulated by stimulus expectations. - Attila Andics; Viktor Gál; Klára Vicsi; Gábor Rudas; Zoltán Vidnyánszky (NeuroImage, 2013)

A Model for Classification Based on the Functional Connectivity Pattern Dynamics of the Brain. - Regina Meszlényi; Ladislav Peska; Viktor Gál; Zoltán Vidnyánszky; Krisztián Búza (ENIC, 2016)

Classification of fMRI data using dynamic time warping based functional connectivity analysis. - Regina Meszlényi; Ladislav Peska; Viktor Gál; Zoltán Vidnyánszky; Krisztián Búza (EUSIPCO, 2016)

Deformable models direct supervised guidance: A novel paradigm for automatic image segmentation. - Nicola Bova; Viktor Gál; Óscar Ibáñez; Oscar Cordón (Neurocomputing, 2016)

Automatic brain MRI motion artifact detection based on end-to-end deep learning is similarly effective as traditional machine learning trained on image quality metrics. - Pál Vakli; Béla Weiss; János Szalma; Peter Barsi; István Gyuricza; Péter Kemenczky; Eszter Somogyi; Ádám Nárai; Viktor Gál; Petra Hermann; Zoltán Vidnyánszky (Medical Image Anal., 2023)