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fMRI-S4: Learning Short- and Long-Range Dynamic fMRI Dependencies Using 1D Convolutions and State Space Models
el-Gazzar, A., Thomas, R. M. & van Wingen, G., 2022, Machine Learning in Clinical Neuroimaging - 5th International Workshop, MLCN 2022, Held in Conjunction with MICCAI 2022, Proceedings. Abdulkadir, A., Bathula, D. R., Dvornek, N. C., Habes, M., Kia, S. M., Kumar, V. & Wolfers, T. (eds.). Springer Science and Business Media Deutschland GmbH, Vol. 13596 LNCS. p. 158-168 11 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 13596 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
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Dynamic Adaptive Spatio-Temporal Graph Convolution for fMRI Modelling
el-Gazzar, A., Thomas, R. M. & van Wingen, G., 2021, Machine Learning in Clinical Neuroimaging - 4th International Workshop, MLCN 2021, Held in Conjunction with MICCAI 2021, Proceedings. Abdulkadir, A., Kia, S. M., Habes, M., Kumar, V., Rondina, J. M., Tax, C. & Wolfers, T. (eds.). Springer Science and Business Media Deutschland GmbH, Vol. 13001 LNCS. p. 125-134 10 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 13001 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
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Classifying Autism Spectrum Disorder Using the Temporal Statistics of Resting-State Functional MRI Data With 3D Convolutional Neural Networks
Thomas, R. M., Gallo, S., Cerliani, L., Zhutovsky, P., el-Gazzar, A. & van Wingen, G., 15 May 2020, In: Frontiers in psychiatry. 11, 440.Research output: Contribution to journal › Article › Academic › peer-review
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