Search Results for author:
Found 13 papers, 1 papers with code
A Novel Stochastic Transformer-based Approach for Post-Traumatic Stress Disorder Detection using Audio Recording of Clinical Interviews
Our proposed approach achieves state-of-the-art performances with an RMSE of 2. 92 on the eDAIC dataset thanks to the stochastic depth, stochastic deep learning layers, and stochastic activation function.
Segmentation of Knee Bones for Osteoarthritis Assessment: A Comparative Analysis of Supervised, Few-Shot, and Zero-Shot Learning Approaches
These findings highlight the effectiveness of few-shot learning for semantic segmentation and the potential of zero-shot learning in enhancing classification models for knee osteoarthritis diagnosis.
Deciphering knee osteoarthritis diagnostic features with explainable artificial intelligence: A systematic review
This paper presents the first survey of XAI techniques used for knee OA diagnosis.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Using U-Net Network for Efficient Brain Tumor Segmentation in MRI Images
Magnetic Resonance Imaging (MRI) is the most commonly used non-intrusive technique for medical image acquisition.
PTSD in the Wild: A Video Database for Studying Post-Traumatic Stress Disorder Recognition in Unconstrained Environments
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 assault, and natural disasters.
Towards a General Deep Feature Extractor for Facial Expression Recognition
In this paper, we propose the Deep Facial Expression Vector ExtractoR (DeepFEVER), a new deep learning-based approach that learns a visual feature extractor general enough to be applied to any other facial emotion recognition task or dataset.
Leveraging Recent Advances in Deep Learning for Audio-Visual Emotion Recognition
In this paper, we propose a new deep learning-based approach for audio-visual emotion recognition.
Ranked #11 on
Facial Expression Recognition (FER)
on AffectNet
(using extra training data)
EEG based Major Depressive disorder and Bipolar disorder detection using Neural Networks: A review
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 of individuals.
Deep Multi-Facial Patches Aggregation Network For Facial Expression Recognition
Results show that the proposed approach achieves state-of-art FER deep learning approaches performance when the model is trained and tested on images from the same dataset.
Clinical Depression and Affect Recognition with EmoAudioNet
Automatic analysis of emotions and affects from speech is an inherently challenging problem with a broad range of applications in Human-Computer Interaction (HCI), health informatics, assistive technologies and multimedia retrieval.
Human-Computer Interaction Sound Audio and Speech Processing
Deep Multi-Facial patches Aggregation Network for Expression Classification from Face Images
Human Expressions is an important key to better link human and computers.
MFCC-based Recurrent Neural Network for Automatic Clinical Depression Recognition and Assessment from Speech
The proposed approach outperforms the state-of-art approaches on the DAIC-WOZ database with an overall accuracy of 76. 27% and a root mean square error of 0. 4 in assessing depression, while a root mean square error of 0. 168 is achieved in predicting the depression severity levels.
3D Geometric salient patterns analysis on 3D meshes
This paper presents a new efficient approach for geometric texture analysis on 3D triangular meshes.
