Search Results for author: Mohamed Ali Mahjoub

Found 18 papers, 7 papers with code

Jedi: Entropy-based Localization and Removal of Adversarial Patches

no code implementations CVPR 2023 Bilel Tarchoun, Anouar Ben Khalifa, Mohamed Ali Mahjoub, Nael Abu-Ghazaleh, Ihsen Alouani

Jedi tackles the patch localization problem from an information theory perspective; leverages two new ideas: (1) it improves the identification of potential patch regions using entropy analysis: we show that the entropy of adversarial patches is high, even in naturalistic patches; and (2) it improves the localization of adversarial patches, using an autoencoder that is able to complete patch regions from high entropy kernels.

StairwayGraphNet for Inter- and Intra-modality Multi-resolution Brain Graph Alignment and Synthesis

1 code implementation6 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.


A Few-shot Learning Graph Multi-Trajectory Evolution Network for Forecasting Multimodal Baby Connectivity Development from a Baseline Timepoint

1 code implementation6 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).

Few-Shot Learning Trajectory Prediction

Non-isomorphic Inter-modality Graph Alignment and Synthesis for Holistic Brain Mapping

1 code implementation30 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.

Graph Generation

Graph Neural Networks in Network Neuroscience

1 code implementation7 Jun 2021 Alaa Bessadok, Mohamed Ali Mahjoub, Islem Rekik

Noninvasive medical neuroimaging has yielded many discoveries about the brain connectivity.

Brain Multigraph Prediction using Topology-Aware Adversarial Graph Neural Network

1 code implementation6 May 2021 Alaa Bessadok, Mohamed Ali Mahjoub, Islem Rekik

Brain graphs (i. e, connectomes) constructed from medical scans such as magnetic resonance imaging (MRI) have become increasingly important tools to characterize the abnormal changes in the human brain.

Graph Generation

Towards New Multiwavelets: Associated Filters and Algorithms. Part I: Theoretical Framework and Investigation of Biomedical Signals, ECG and Coronavirus Cases

no code implementations9 Mar 2021 Malika Jallouli, Makerem Zemni, Anouar Ben Mabrouk, Mohamed Ali Mahjoub

Biosignals are nowadays important subjects for scientific researches from both theory and applications especially with the appearance of new pandemics threatening humanity such as the new Coronavirus.

Supervised Multi-topology Network Cross-diffusion for Population-driven Brain Network Atlas Estimation

2 code implementations23 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.

feature selection

Topology-Aware Generative Adversarial Network for Joint Prediction of Multiple Brain Graphs from a Single Brain Graph

1 code implementation23 Sep 2020 Alaa Bessadok, Mohamed Ali Mahjoub, Islem Rekik

Several works based on Generative Adversarial Networks (GAN) have been recently proposed to predict a set of medical images from a single modality (e. g, FLAIR MRI from T1 MRI).

Graph Generation

A Comparative Study of Filtering Approaches Applied to Color Archival Document Images

no code implementations16 Aug 2019 Walid Elhedda, Maroua Mehri, Mohamed Ali Mahjoub

Current systems used by the Tunisian national archives for the automatic transcription of archival documents are hindered by many issues related to the performance of the optical character recognition (OCR) tools.

Denoising Image Enhancement +1

Adaptive strategy for superpixel-based region-growing image segmentation

no code implementations17 Mar 2018 Mahaman Sani Chaibou, Pierre-Henri Conze, Karim Kalti, Basel Solaiman, Mohamed Ali Mahjoub

From an initial contour-constrained over-segmentation of the input image, the image segmentation is achieved by iteratively merging similar superpixels into regions.

Image Segmentation Semantic Segmentation +1

Dynamic Multiscale Tree Learning Using Ensemble Strong Classifiers for Multi-label Segmentation of Medical Images with Lesions

no code implementations5 Sep 2017 Samya Amiri, Mohamed Ali Mahjoub, Islem Rekik

Unlike previous works that simply aggregate or cascade classifiers for addressing image segmentation and labeling tasks, we propose to embed strong classifiers into a tree structure that allows bi-directional flow of information between its classifier nodes to gradually improve their performances.

General Classification Image Segmentation +2

Mouse Movement and Probabilistic Graphical Models Based E-Learning Activity Recognition Improvement Possibilistic Model

no code implementations8 Aug 2016 Anis Elbahi, Mohamed Nazih Omri, Mohamed Ali Mahjoub, Kamel Garrouch

Probabilistic graphical models such as hidden Markov models and conditional random fields have been successfully used in order to identify a Web users activity.

Activity Recognition

A combined Approach Based on Fuzzy Classification and Contextual Region Growing to Image Segmentation

no code implementations8 Aug 2016 Mahaman Sani Chaibou, Karim Kalti, Bassel Soulaiman, Mohamed Ali Mahjoub

This allows the proposed approach to operate at high level instead of using low-level features and consequently to remedy to the problem of the semantic gap.

General Classification Image Segmentation +1

Dynamic Hierarchical Bayesian Network for Arabic Handwritten Word Recognition

no code implementations20 May 2014 Khaoula jayech, Nesrine Trimech, Mohamed Ali Mahjoub, Najoua Essoukri Ben Amara

This paper presents a new probabilistic graphical model used to model and recognize words representing the names of Tunisian cities.


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