1 code implementation • 6 Oct 2021 • Alaa Bessadok, Ahmed Nebli, Mohamed Ali Mahjoub, Gang Li, Weili Lin, Dinggang Shen, Islem Rekik
To the best of our knowledge, this is the first teacher-student architecture tailored for brain graph multi-trajectory growth prediction that is based on few-shot learning and generalized to graph neural networks (GNNs).
1 code implementation • 6 Oct 2021 • Alpay Tekin, Ahmed Nebli, Islem Rekik
Several brain disorders can be detected by observing alterations in the brain's structural and functional connectivities.
1 code implementation • 30 Jun 2021 • Islem Mhiri, Ahmed Nebli, Mohamed Ali Mahjoub, Islem Rekik
Our three core contributions lie in (i) predicting a target graph (e. g., functional) from a source graph (e. g., morphological) based on a novel graph generative adversarial network (gGAN); (ii) using non-isomorphic graphs for both source and target domains with a different number of nodes, edges and structure; and (iii) enforcing the predicted target distribution to match that of the ground truth graphs using a graph autoencoder to relax the designed loss oprimization.
1 code implementation • 28 Sep 2020 • Ahmed Nebli, Ugur Ali Kaplan, Islem Rekik
Our EvoGraphNet architecture cascades a set of time-dependent gGANs, where each gGAN communicates its predicted brain graphs at a particular timepoint to train the next gGAN in the cascade at follow-up timepoint.
1 code implementation • 24 Sep 2020 • Ahmed Nebli, Islem Rekik
Differently, in this paper, we tap into the nascent field of geometric-GANs (G-GAN) to design a deep multiplex prediction architecture comprising (i) a geometric source to target network translator mimicking a U-Net architecture with skip connections and (ii) a conditional discriminator which classifies predicted target intra-layers by conditioning on the multiplex source intra-layers.
1 code implementation • 23 Sep 2020 • Zeynep Gurler, Ahmed Nebli, Islem Rekik
We use these embeddings to compute the similarity between training and testing subjects which allows us to pick the closest training subjects at baseline timepoint to predict the evolution of the testing brain graph over time.