264 citations · 806 across the 17 of their papers we have counts for
18 papers
Leakage-Controlled Multimodal Learning for Diagnosis and Progression Prediction in Alzheimer's Disease Research
Akeem Temitope Otapo, Ghazaleh Khodabandelou, Zuheng Ming +1
Alzheimer's disease prediction involves irregular visits, heterogeneous measurements and incomplete modalities. This study presents a multimodal multitask framework combining an ad…
Leveraging Auto-Distillation and Generative Self-Supervised Learning in Residual Graph Transformers for Enhanced Recommender Systems
Eya Mhedhbi, Youssef Mourchid, Alice Othmani
This paper introduces a cutting-edge method for enhancing recommender systems through the integration of generative self-supervised learning (SSL) with a Residual Graph Transformer…
Prediction and Detection of Terminal Diseases Using Internet of Medical Things: A Review
Akeem Temitope Otapo, Alice Othmani, Ghazaleh Khodabandelou +1
The integration of Artificial Intelligence (AI) and the Internet of Medical Things (IoMT) in healthcare, through Machine Learning (ML) and Deep Learning (DL) techniques, has advanc…
A Novel Stochastic Transformer-based Approach for Post-Traumatic Stress Disorder Detection using Audio Recording of Clinical Interviews
Mamadou Dia, Ghazaleh Khodabandelou, Alice Othmani
Post-traumatic stress disorder (PTSD) is a mental disorder that can be developed after witnessing or experiencing extremely traumatic events. PTSD can affect anyone, regardless of…
Segmentation of Knee Bones for Osteoarthritis Assessment: A Comparative Analysis of Supervised, Few-Shot, and Zero-Shot Learning Approaches
Yun Xin Teoh, Alice Othmani, Siew Li Goh +2
Knee osteoarthritis is a degenerative joint disease that induces chronic pain and disability. Bone morphological analysis is a promising tool to understand the mechanical aspect of…
Deciphering knee osteoarthritis diagnostic features with explainable artificial intelligence: A systematic review
Yun Xin Teoh, Alice Othmani, Siew Li Goh +2
Existing artificial intelligence (AI) models for diagnosing knee osteoarthritis (OA) have faced criticism for their lack of transparency and interpretability, despite achieving med…
An Ambient Intelligence-based Approach For Longitudinal Monitoring of Verbal and Vocal Depression Symptoms
Alice Othmani, Muhammad Muzammel
Automatic speech recognition (ASR) technology can aid in the detection, monitoring, and assessment of depressive symptoms in individuals. ASR systems have been used as a tool to an…
Using U-Net Network for Efficient Brain Tumor Segmentation in MRI Images
Jason Walsh, Alice Othmani, Mayank Jain +1
Magnetic Resonance Imaging (MRI) is the most commonly used non-intrusive technique for medical image acquisition. Brain tumor segmentation is the process of algorithmically identif…
PTSD in the Wild: A Video Database for Studying Post-Traumatic Stress Disorder Recognition in Unconstrained Environments
Moctar Abdoul Latif Sawadogo, Furkan Pala, Gurkirat Singh +3
POST-traumatic stress disorder (PTSD) is a chronic and debilitating mental condition that is developed in response to catastrophic life events, such as military combat, sexual assa…
Towards a General Deep Feature Extractor for Facial Expression Recognition
Liam Schoneveld, Alice Othmani
The human face conveys a significant amount of information. Through facial expressions, the face is able to communicate numerous sentiments without the need for verbalisation. Visu…
Leveraging Recent Advances in Deep Learning for Audio-Visual Emotion Recognition
Liam Schoneveld, Alice Othmani, Hazem Abdelkawy
Emotional expressions are the behaviors that communicate our emotional state or attitude to others. They are expressed through verbal and non-verbal communication. Complex human be…
AudVowelConsNet: A Phoneme-Level Based Deep CNN Architecture for Clinical Depression Diagnosis
Muhammad Muzammel, Hanan Salam, Yann Hoffmann +2
Depression is a common and serious mood disorder that negatively affects the patient's capacity of functioning normally in daily tasks. Speech is proven to be a vigorous tool in de…
EEG based Major Depressive disorder and Bipolar disorder detection using Neural Networks: A review
Sana Yasin, Syed Asad Hussain, Sinem Aslan +3
Mental disorders represent critical public health challenges as they are leading contributors to the global burden of disease and intensely influence social and financial welfare o…
Deep Multi-Facial Patches Aggregation Network For Facial Expression Recognition
Ahmed Rachid Hazourli, Amine Djeghri, Hanan Salam +1
In this paper, we propose an approach for Facial Expressions Recognition (FER) based on a deep multi-facial patches aggregation network. Deep features are learned from facial patch…
Towards Robust Deep Neural Networks for Affect and Depression Recognition from Speech
Alice Othmani, Daoud Kadoch, Kamil Bentounes +3
Intelligent monitoring systems and affective computing applications have emerged in recent years to enhance healthcare. Examples of these applications include assessment of affecti…
Deep Multi-Facial patches Aggregation Network for Expression Classification from Face Images
Amine Djerghri, Ahmed Rachid Hazourli, Alice Othmani
Emotional Intelligence in Human-Computer Interaction has attracted increasing attention from researchers in multidisciplinary research fields including psychology, computer vision,…
MFCC-based Recurrent Neural Network for Automatic Clinical Depression Recognition and Assessment from Speech
Emna Rejaibi, Ali Komaty, Fabrice Meriaudeau +2
Clinical depression or Major Depressive Disorder (MDD) is a common and serious medical illness. In this paper, a deep recurrent neural network-based framework is presented to detec…
3D Geometric salient patterns analysis on 3D meshes
Alice Othmani, Fakhri Torkhani, Jean-Marie Favreau
Pattern analysis is a wide domain that has wide applicability in many fields. In fact, texture analysis is one of those fields, since the texture is defined as a set of repetitive…