5 citations · 6 across the 14 of their papers we have counts for
14 papers
AffectFlow-DINO: Uncertainty-Aware Multi-Task Affect Estimation via Conditional Rectified Flow
Salah Eddine Bekhouche, Abdellah Zakaria Sellam, Fadi Dornaika +1
We present \textbf{AffectFlow-DINO}, a multi-task learning system for the 11th ABAW challenge that extends a standard deterministic architecture with a conditional rectified-flow h…
TMF-RSE: Tri-Modal Fusion with Regional Semantics and Evidential Uncertainty for Lung Severity Scoring
Fadi Abdeladhim Zidi, Salah Eddine Bekhouche, Abdellah Zakaria Sellam +3
Accurate quantification of lung disease severity from chest imaging is critical for clinical decision-making and resource allocation. We propose a tri-modal deep learning framework…
VLM-PAR: A Vision Language Model for Pedestrian Attribute Recognition
Abdellah Zakaria Sellam, Salah Eddine Bekhouche, Fadi Dornaika +2
Pedestrian Attribute Recognition (PAR) involves predicting fine-grained attributes such as clothing color, gender, and accessories from pedestrian imagery, yet is hindered by sever…
SCS-SupCon: Sigmoid-based Common and Style Supervised Contrastive Learning with Adaptive Decision Boundaries
Bin Wang, Fadi Dornaika
Image classification is hindered by subtle inter-class differences and substantial intra-class variations, which limit the effectiveness of existing contrastive learning methods. S…
Model-Agnostic Fairness Regularization for GNNs with Incomplete Sensitive Information
Mahdi Tavassoli Kejani, Fadi Dornaika, Jean-Michel Loubes
Graph Neural Networks (GNNs) have demonstrated exceptional efficacy in relational learning tasks, including node classification and link prediction. However, their application rais…
MambaCAFU: Hybrid Multi-Scale and Multi-Attention Model with Mamba-Based Fusion for Medical Image Segmentation
T-Mai Bui, Fares Bougourzi, Fadi Dornaika +1
In recent years, deep learning has shown near-expert performance in segmenting complex medical tissues and tumors. However, existing models are often task-specific, with performanc…
A supervised discriminant data representation: application to pattern classification
Fadi Dornaika, Ahmad Khoder, Abdelmalik Moujahid +1
The performance of machine learning and pattern recognition algorithms generally depends on data representation. That is why, much of the current effort in performing machine learn…
Lung Infection Severity Prediction Using Transformers with Conditional TransMix Augmentation and Cross-Attention
Bouthaina Slika, Fadi Dornaika, Fares Bougourzi +1
Lung infections, particularly pneumonia, pose serious health risks that can escalate rapidly, especially during pandemics. Accurate AI-based severity prediction from medical imagin…
Enhanced Arabic Text Retrieval with Attentive Relevance Scoring
Salah Eddine Bekhouche, Azeddine Benlamoudi, Yazid Bounab +2
Arabic poses a particular challenge for natural language processing (NLP) and information retrieval (IR) due to its complex morphology, optional diacritics and the coexistence of M…
D-TrAttUnet: Toward Hybrid CNN-Transformer Architecture for Generic and Subtle Segmentation in Medical Images
Fares Bougourzi, Fadi Dornaika, Cosimo Distante +1
Over the past two decades, machine analysis of medical imaging has advanced rapidly, opening up significant potential for several important medical applications. As complicated dis…
Artificial Intelligence in Bone Metastasis Analysis: Current Advancements, Opportunities and Challenges
Marwa Afnouch, Fares Bougourzi, Olfa Gaddour +2
In recent years, Artificial Intelligence (AI) has been widely used in medicine, particularly in the analysis of medical imaging, which has been driven by advances in computer visio…
Rethinking Attention Gated with Hybrid Dual Pyramid Transformer-CNN for Generalized Segmentation in Medical Imaging
Fares Bougourzi, Fadi Dornaika, Abdelmalik Taleb-Ahmed +1
Inspired by the success of Transformers in Computer vision, Transformers have been widely investigated for medical imaging segmentation. However, most of Transformer architecture a…
Fair Graph Neural Network with Supervised Contrastive Regularization
Mahdi Tavassoli Kejani, Fadi Dornaika, Jean-Michel Loubes
In recent years, Graph Neural Networks (GNNs) have made significant advancements, particularly in tasks such as node classification, link prediction, and graph representation. Howe…
Ensembling and Test Augmentation for Covid-19 Detection and Covid-19 Domain Adaptation from 3D CT-Scans
Fares Bougourzi, Feryal Windal Moula, Halim Benhabiles +2
Since the emergence of Covid-19 in late 2019, medical image analysis using artificial intelligence (AI) has emerged as a crucial research area, particularly with the utility of CT-…