40 citations · 72 across the 15 of their papers we have counts for
15 papers
CT to PET Translation: A Large-scale Dataset and Domain-Knowledge-Guided Diffusion Approach
Dac Thai Nguyen, Trung Thanh Nguyen, Huu Tien Nguyen +5
Positron Emission Tomography (PET) and Computed Tomography (CT) are essential for diagnosing, staging, and monitoring various diseases, particularly cancer. Despite their importanc…
FedMAC: Tackling Partial-Modality Missing in Federated Learning with Cross-Modal Aggregation and Contrastive Regularization
Manh Duong Nguyen, Trung Thanh Nguyen, Huy Hieu Pham +3
Federated Learning (FL) is a method for training machine learning models using distributed data sources. It ensures privacy by allowing clients to collaboratively learn a shared gl…
FedGrad: Mitigating Backdoor Attacks in Federated Learning Through Local Ultimate Gradients Inspection
Thuy Dung Nguyen, Anh Duy Nguyen, Kok-Seng Wong +4
Federated learning (FL) enables multiple clients to train a model without compromising sensitive data. The decentralized nature of FL makes it susceptible to adversarial attacks, e…
High Accurate and Explainable Multi-Pill Detection Framework with Graph Neural Network-Assisted Multimodal Data Fusion
Anh Duy Nguyen, Huy Hieu Pham, Huynh Thanh Trung +3
Due to the significant resemblance in visual appearance, pill misuse is prevalent and has become a critical issue, responsible for one-third of all deaths worldwide. Pill identific…
Machine learning-based detection of cardiovascular disease using ECG signals: performance vs. complexity
Huy Pham, Konstantin Egorov, Alexey Kazakov +1
Cardiovascular disease remains a significant problem in modern society. Among non-invasive techniques, the electrocardiogram (ECG) is one of the most reliable methods for detecting…
CADIS: Handling Cluster-skewed Non-IID Data in Federated Learning with Clustered Aggregation and Knowledge DIStilled Regularization
Nang Hung Nguyen, Duc Long Nguyen, Trong Bang Nguyen +4
Federated learning enables edge devices to train a global model collaboratively without exposing their data. Despite achieving outstanding advantages in computing efficiency and pr…
A novel deep learning-based approach for sleep apnea detection using single-lead ECG signals
Anh-Tu Nguyen, Thao Nguyen, Huy-Khiem Le +2
Sleep apnea (SA) is a type of sleep disorder characterized by snoring and chronic sleeplessness, which can lead to serious conditions such as high blood pressure, heart failure, an…
A Novel Approach for Pill-Prescription Matching with GNN Assistance and Contrastive Learning
Trung Thanh Nguyen, Hoang Dang Nguyen, Thanh Hung Nguyen +3
Medication mistaking is one of the risks that can result in unpredictable consequences for patients. To mitigate this risk, we develop an automatic system that correctly identifies…
Image-based Contextual Pill Recognition with Medical Knowledge Graph Assistance
Anh Duy Nguyen, Thuy Dung Nguyen, Huy Hieu Pham +2
Identifying pills given their captured images under various conditions and backgrounds has been becoming more and more essential. Several efforts have been devoted to utilizing the…
FedDRL: Deep Reinforcement Learning-based Adaptive Aggregation for Non-IID Data in Federated Learning
Nang Hung Nguyen, Phi Le Nguyen, Duc Long Nguyen +4
The uneven distribution of local data across different edge devices (clients) results in slow model training and accuracy reduction in federated learning. Naive federated learning…
Learning to Automatically Diagnose Multiple Diseases in Pediatric Chest Radiographs Using Deep Convolutional Neural Networks
Thanh T. Tran, Hieu H. Pham, Thang V. Nguyen +3
Chest radiograph (CXR) interpretation in pediatric patients is error-prone and requires a high level of understanding of radiologic expertise. Recently, deep convolutional neural n…
A Unified Deep Framework for Joint 3D Pose Estimation and Action Recognition from a Single RGB Camera
Huy Hieu Pham, Houssam Salmane, Louahdi Khoudour +3
We present a deep learning-based multitask framework for joint 3D human pose estimation and action recognition from RGB video sequences. Our approach proceeds along two stages. In…
Learning to Recognize 3D Human Action from A New Skeleton-based Representation Using Deep Convolutional Neural Networks
Huy-Hieu Pham, Louahdi Khoudour, Alain Crouzil +2
Recognizing human actions in untrimmed videos is an important challenging task. An effective 3D motion representation and a powerful learning model are two key factors influencing…
Skeletal Movement to Color Map: A Novel Representation for 3D Action Recognition with Inception Residual Networks
Huy Hieu Pham, Louahdi Khoudour, Alain Crouzil +2
We propose a novel skeleton-based representation for 3D action recognition in videos using Deep Convolutional Neural Networks (D-CNNs). Two key issues have been addressed: First, h…
Learning and Recognizing Human Action from Skeleton Movement with Deep Residual Neural Networks
Huy-Hieu Pham, Louahdi Khoudour, Alain Crouzil +2
Automatic human action recognition is indispensable for almost artificial intelligent systems such as video surveillance, human-computer interfaces, video retrieval, etc. Despite a…