A5065112274- COVID-19 diagnosis using AI
- Radiomics and Machine Learning in Medical Imaging
- Lung Cancer Diagnosis and Treatment
- AI in cancer detection
- Anomaly Detection Techniques and Applications
- ECG Monitoring and Analysis
- Privacy-Preserving Technologies in Data
- EEG and Brain-Computer Interfaces
- Advanced X-ray and CT Imaging
- Pneumonia and Respiratory Infections
- Digital Radiography and Breast Imaging
- Advanced Neural Network Applications
- Medical Imaging and Analysis
- Brain Tumor Detection and Classification
- Radiology practices and education
- Human Pose and Action Recognition
- Global Cancer Incidence and Screening
- Biofuel production and bioconversion
- Domain Adaptation and Few-Shot Learning
- Data-Driven Disease Surveillance
- Internet Traffic Analysis and Secure E-voting
- Blockchain Technology Applications and Security
- Infectious Diseases and Tuberculosis
- Adversarial Robustness in Machine Learning
- Stochastic Gradient Optimization Techniques
VinUniversity
2019-2025
Academy of Cryptography Techniques
2024
Viet Duc Hospital
2024
University of Illinois Urbana-Champaign
2023
Institut de Recherche en Informatique de Toulouse
2019-2022
Université Toulouse III - Paul Sabatier
2019-2022
Universidad Carlos III de Madrid
2022
Centre d'Études et d'Expertise sur les Risques, l'Environnement, la Mobilité et l'Aménagement
2019-2022
Universidad de Los Andes, Chile
2022
Ho Chi Minh City University of Technology
2020-2021
Most of the existing chest X-ray datasets include labels from a list findings without specifying their locations on radiographs. This limits development machine learning algorithms for detection and localization abnormalities. In this work, we describe dataset more than 100,000 scans that were retrospectively collected two major hospitals in Vietnam. Out raw data, release 18,000 images manually annotated by total 17 experienced radiologists with 22 local rectangles surrounding abnormalities...
Abstract Mammography, or breast X-ray imaging, is the most widely used imaging modality to detect cancer and other diseases. Recent studies have shown that deep learning-based computer-assisted detection diagnosis (CADe/x) tools been developed support physicians improve accuracy of interpreting mammography. A number large-scale mammography datasets from different populations with various associated annotations clinical data introduced study potential methods in field radiology. With aim...
Abstract Difficulties in the production of lignin from rice straw because high silica content recovered reduce its recovery yield and applications as bio-fuel aromatic chemicals. Therefore, objective this study is to develop a novel method more effectively selectively. The established by monitoring precipitation behavior well chemical structure precipitate single-stage acidification at different pH values black liquor collected alkaline treatment straw. result illustrates significant...
Abstract Computer-aided diagnosis systems in adult chest radiography (CXR) have recently achieved great success thanks to the availability of large-scale, annotated datasets and advent high-performance supervised learning algorithms. However, development diagnostic models for detecting diagnosing pediatric diseases CXR scans is undertaken due lack high-quality physician-annotated datasets. To overcome this challenge, we introduce release PediCXR, a new dataset 9,125 studies retrospectively...
Recent years have experienced phenomenal growth in computer-aided diagnosis systems based on machine learning algorithms for anomaly detection tasks the medical image domain. However, performance of these greatly depends quality labels since subjectivity a single annotator might decline certainty datasets. In order to alleviate this problem, aggregating from multiple radiologists with different levels expertise has been established. particular, under reliance their own biases and proficiency...
Advanced deep learning (DL) algorithms may predict the patient's risk of developing breast cancer based on Breast Imaging Reporting and Data System (BI-RADS) density standards. Recent studies have suggested that combination multi-view analysis improved overall exam classification. In this paper, we propose a novel DL approach for BI-RADS assessment mammograms. The proposed first deploys convolutional networks feature extraction each view separately. extracted features are then stacked fed...
ABSTRACT Mammography, or breast X-ray, is the most widely used imaging modality to detect cancer and other diseases. Recent studies have shown that deep learning-based computer-assisted detection diagnosis (CADe/x) tools been developed support physicians improve accuracy of interpreting mammography. However, published datasets mammography are either limited on sample size digitalized from screen-film (SFM), hindering development CADe/x which based full-field digital (FFDM). To overcome this...
Abstract Chest radiography is one of the most common types diagnostic radiology exams, which critical for screening and diagnosis many different thoracic diseases. Specialized algorithms have been developed to detect several specific pathologies such as lung nodule or cancer. However, accurately detecting presence multiple diseases from chest X-rays (CXRs) still a challenging task. This paper presents supervised multi-label classification framework based on deep convolutional neural networks...
Designing motion representations for 3D human action recognition from skeleton sequences is an important yet challenging task. An effective representation should be robust to noise, invariant viewpoint changes and result in a good performance with low-computational demand. Two main challenges this task include how efficiently represent spatio–temporal patterns of skeletal movements learn their discriminative features classification tasks. This paper presents novel skeleton-based deep...
Most of the existing chest X-ray datasets include labels from a list findings without specifying their locations on radiographs. This limits development machine learning algorithms for detection and localization abnormalities. In this work, we describe dataset more than 100,000 scans that were retrospectively collected two major hospitals in Vietnam. Out raw data, release 18,000 images manually annotated by total 17 experienced radiologists with 22 local rectangles surrounding abnormalities...
This paper introduces SEMISE, a novel method for representation learning in medical imaging that combines self-supervised and supervised learning. By leveraging both labeled augmented data, SEMISE addresses the challenge of data scarcity enhances encoder's ability to extract meaningful features. integrated approach leads more informative representations, improving performance on downstream tasks. As result, our achieved 12% improvement classification 3% segmentation, outperforming existing...
This paper addresses the few-shot image classification problem, where task is performed on unlabeled query samples given a small amount of labeled support only. One major challenge learning problem large variety object visual appearances that prevents to represent comprehensively. might result in significant difference between and samples, therefore undermining performance algorithms. In this paper, we tackle by proposing Few-shot Cosine Transformer (FS-CT), relational map supports queries...
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 classification tasks. However, only few studies addressed the localization abnormal findings from CXR scans, which in explaining image-level to radiologists. Additionally, actual impact AI diagnostic radiologists clinical practice remains relatively unclear. To bridge...
A fully automated system for interpreting abdominal computed tomography (CT) scans with multiple phases of contrast enhancement requires an accurate classification the phases. Current approaches to classify CT are commonly based on three-dimensional (3D) convolutional neural network (CNN) high computational complexity and latency. This work aims at developing validating a precise, fast multiphase classifier recognize three main types in scans.We propose this study novel method that uses...
The COVID-19 pandemic has exposed the vulnerability of healthcare services worldwide, raising need to develop novel tools provide rapid and cost-effective screening diagnosis. Clinical reports indicated that infection may cause cardiac injury, electrocardiograms (ECG) serve as a diagnostic biomarker for COVID-19. This study aims utilize ECG signals detect automatically. We propose method extract from paper records, which are then fed into one-dimensional convolution neural network (1D-CNN)...