1 code implementation • 6 Oct 2021 • Islem Mhiri, Mohamed Ali Mahjoub, Islem Rekik
Our SG-Net is grounded in three main contributions: (i) predicting a target graph from a source one based on a novel graph generative adversarial network in both inter (e. g., morphological-functional) and intra (e. g., functional-functional) domains, (ii) generating high-resolution brain graphs without resorting to the time consuming and expensive MRI processing steps, and (iii) enforcing the source distribution to match that of the ground truth graphs using an inter-modality aligner to relax the loss function to optimize.
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.
2 code implementations • 23 Sep 2020 • Islem Mhiri, Mohamed Ali Mahjoub, Islem Rekik
Estimating a representative and discriminative brain network atlas (BNA) is a nascent research field in mapping a population of brain networks in health and disease.