13 citations · 32 across the 8 of their papers we have counts for
8 papers
Hybrid Deep Learning-Based for Enhanced Occlusion Segmentation in PICU Patient Monitoring
Mario Francisco Munoz, Hoang Vu Huy, Thanh-Dung Le
Remote patient monitoring has emerged as a prominent non-invasive method, using digital technologies and computer vision (CV) to replace traditional invasive monitoring. While neon…
Label Propagation Techniques for Artifact Detection in Imbalanced Classes using Photoplethysmogram Signals
Clara Macabiau, Thanh-Dung Le, Kevin Albert +3
This study aimed to investigate the application of label propagation techniques to propagate labels among photoplethysmogram (PPG) signals, particularly in imbalanced class scenari…
GRN-Transformer: Enhancing Motion Artifact Detection in PICU Photoplethysmogram Signals
Thanh-Dung Le, Clara Macabiau, Kévin Albert +2
Photoplethysmogram (PPG) signals, optical measurements of pulsatile blood flow used continuously in intensive care monitoring, are frequently contaminated by motion, low perfusion,…
Improving Transformer Performance for French Clinical Notes Classification Using Mixture of Experts on a Limited Dataset
Thanh-Dung Le, Philippe Jouvet, Rita Noumeir
Transformer-based models have shown outstanding results in natural language processing but face challenges in applications like classifying small-scale clinical texts, especially w…
Adaptation of Autoencoder for Sparsity Reduction From Clinical Notes Representation Learning
Thanh-Dung Le, Rita Noumeir, Jerome Rambaud +2
When dealing with clinical text classification on a small dataset recent studies have confirmed that a well-tuned multilayer perceptron outperforms other generative classifiers, in…
Detecting of a Patient's Condition From Clinical Narratives Using Natural Language Representation
Thanh-Dung Le, Rita Noumeir, Jerome Rambaud +2
The rapid progress in clinical data management systems and artificial intelligence approaches enable the era of personalized medicine. Intensive care units (ICUs) are the ideal cli…
Machine Learning Based on Natural Language Processing to Detect Cardiac Failure in Clinical Narratives
Thanh-Dung Le, Rita Noumeir, Jerome Rambaud +2
The purpose of the study presented herein is to develop a machine learning algorithm based on natural language processing that automatically detects whether a patient has a cardiac…
Reproducing AmbientGAN: Generative models from lossy measurements
Mehdi Ahmadi, Timothy Nest, Mostafa Abdelnaim +1
In recent years, Generative Adversarial Networks (GANs) have shown substantial progress in modeling complex distributions of data. These networks have received tremendous attention…