Browsing Pham Huy Hieu, PhD. by Issue Date
Now showing items 1-20 of 27
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VinDr-SpineXR: A deep learning framework for spinal lesions detection and classification from radiographs
(2021-06-24)Radiographs are used as the most important imaging tool for identifying spine anomalies in clinical practice. The evaluation of spinal bone lesions, however, is a challenging task for radiologists. This work aims at ... -
Learning to Automatically Diagnose Multiple Diseases in Pediatric Chest Radiographs Using Deep Convolutional Neural Networks
(2021-10-17)Chest radiograph (CXR) interpretation is critical for the diagnosis of various thoracic diseases in pediatric patients. This task, however, is error-prone and requires a high level of understanding of radiologic expertise. ... -
A novel multi-view deep learning approach for BI-RADS and density assessment of mammograms
(2022)Advanced deep learning (DL) algorithms may predict the patient’s risk of developing breast cancer based on the Breast Imaging Reporting and Data System (BI-RADS) and density standards. Recent studies have suggested that ... -
VinDr-CXR: An open dataset of chest X-rays with radiologist’s annotations
(2022)Most of the existing chest X-ray datasets include labels from a list of findings without specifying their locations on the radiographs. This limits the development of machine learning algorithms for the detection and ... -
Image-based contextual pill recognition with medical knowledge graph assistance
(2022)In many healthcare applications, accurately identifying pills from images captured under varying conditions has become increasingly crucial. Despite numerous attempts to employ deep learning methods for pill recognition, ... -
Multi-stream fusion for class incremental learning in pill image classification
(2022)Classifying pill categories from real-world images is crucial for various smart healthcare applications. Although existing approaches in image classification might achieve a good performance on fixed pill categories, they ... -
A novel approach for pill-prescription matching with GNN assistance and contrastive learning
(2022)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 pill-prescription from mobile images. ... -
Phase recognition in contrast-enhanced CT scans based on deep learning and random sampling
(2022-01-31)Purpose: A fully automated system for interpreting abdominal computed tomography (CT) scans with multiple phases of contrast enhancement requires accurate classification of the phases. Current approaches typically utilize ... -
A Novel Transparency Strategy-based Data Augmentation Approach for BI-RADS Classification of Mammograms
(2022-03-20)Image augmentation techniques have been extensively studied to enhance the performance of deep learning (DL) algorithms in mammography classification tasks. Recent advancements have demonstrated the effectiveness of image ... -
Learning from multiple expert annotators for enhancing anomaly detection in medical image analysis
(2022-03-20)Building an accurate computer-aided diagnosis system based on data-driven approaches requires a large amount of high-quality labeled data. In medical imaging analysis, multiple expert annotators often produce subjective ... -
LightX3ECG: A lightweight and explainable deep learning system for 3-lead electrocardiogram classification
(2022-07-25)Cardiovascular diseases (CVDs) are a group of heart and blood vessel disorders that is one of the most serious dangers to human health, and the number of such patients is still growing. Early and accurate detection plays ... -
Deployment and validation of an AI system for detecting abnormal chest radiographs in clinical settings
(2022-07-27)Background: The purpose of this paper is to demonstrate a mechanism for deploying and validating an AI-based system for detecting abnormalities on chest X-ray scans at the Phu Tho General Hospital, Vietnam. We aim to ... -
Deployment and validation of an AI system for detecting abnormal chest radiographs in clinical settings
(2022-07-27)Background: The purpose of this paper is to demonstrate a mechanism for deploying and validating an AI-based system for detecting abnormalities on chest X-ray scans at the Phu Tho General Hospital, Vietnam. We aim to ... -
FedDRL: Deep reinforcement learning-based adaptive aggregation for non-IID data in federated learning
(2022-08-04)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 (FL) strategy and most alternative solutions ... -
An Accurate and Explainable Deep Learning System Improves Interobserver Agreement in the Interpretation of Chest Radiograph
(2022-08-06)Interpretation of chest radiographs (CXR) is a difficult but essential task for detecting thoracic abnormalities. Recent artificial intelligence (AI) algorithms have achieved radiologist-level performance on various medical ... -
Enhancing deep learning-based 3-lead ECG classification with heartbeat counting and demographic data integration
(2022-08-15)Nowadays, an increasing number of people are being diagnosed with cardiovascular diseases (CVDs), the leading cause of death globally. The gold standard for identifying these heart problems is via electrocardiogram (ECG). ... -
An accurate and explainable deep learning system improves interobserver agreement in the interpretation of chest radiograph
(2022-10-04)Interpretation of chest radiographs (CXR) is a difficult but essential task for detecting thoracic abnormalities. Recent artificial intelligence (AI) algorithms have achieved radiologist-level performance on various medical ... -
Learning to diagnose common thorax diseases on chest radiographs from radiology reports in Vietnamese
(2022-10-07)Deep learning, in recent times, has made remarkable strides when it comes to impressive performance for many tasks, including medical image processing. One of the contributing factors to these advancements is the emergence ... -
Detecting COVID-19 from digitized ECG printouts using 1D convolutional neural networks
(2022-11)The COVID-19 pandemic has exposed the vulnerability of healthcare services worldwide, raising the need to develop novel tools to provide rapid and cost-effective screening and diagnosis. Clinical reports indicated that ... -
VinDr-CXR: An open dataset of chest X-rays with radiologist’s annotations
(2022-12)Most of the existing chest X-ray datasets include labels from a list of findings without specifying their locations on the radiographs. This limits the development of machine learning algorithms for the detection and ...
