33 citations · 111 across the 21 of their papers we have counts for
23 papers
Evaluating the impact of an explainable machine learning system on the interobserver agreement in chest radiograph interpretation
Hieu H. Pham, Ha Q. Nguyen, Hieu T. Nguyen +2
We conducted a prospective study to measure the clinical impact of an explainable machine learning system on interobserver agreement in chest radiograph interpretation. The AI syst…
Improving Object Detection in Medical Image Analysis through Multiple Expert Annotators: An Empirical Investigation
Hieu H. Pham, Khiem H. Le, Tuan V. Tran +1
The work discusses the use of machine learning algorithms for anomaly detection in medical image analysis and how the performance of these algorithms depends on the number of annot…
Learning to diagnose common thorax diseases on chest radiographs from radiology reports in Vietnamese
Thao T. B. Nguyen, Tam M. Vo, Thang V. Nguyen +2
We propose a data collecting and annotation pipeline that extracts information from Vietnamese radiology reports to provide accurate labels for chest X-ray (CXR) images. This can b…
An Accurate and Explainable Deep Learning System Improves Interobserver Agreement in the Interpretation of Chest Radiograph
Hieu H. Pham, Ha Q. Nguyen, Hieu T. Nguyen +2
Recent artificial intelligence (AI) algorithms have achieved radiologist-level performance on various medical classification tasks. However, only a few studies addressed the locali…
Slice-level Detection of Intracranial Hemorrhage on CT Using Deep Descriptors of Adjacent Slices
Dat T. Ngo, Thao T. B. Nguyen, Hieu T. Nguyen +3
The rapid development in representation learning techniques such as deep neural networks and the availability of large-scale, well-annotated medical imaging datasets have to a rapi…
Phase Recognition in Contrast-Enhanced CT Scans based on Deep Learning and Random Sampling
Binh T. Dao, Thang V. Nguyen, Hieu H. Pham +1
A fully automated system for interpreting abdominal computed tomography (CT) scans with multiple phases of contrast enhancement requires an accurate classification of the phases. T…
Learning from Multiple Expert Annotators for Enhancing Anomaly Detection in Medical Image Analysis
Khiem H. Le, Tuan V. Tran, Hieu H. Pham +3
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 exp…
VinDr-Mammo: A large-scale benchmark dataset for computer-aided diagnosis in full-field digital mammography
Hieu T. Nguyen, Ha Q. Nguyen, Hieu H. Pham +4
Mammography, or breast X-ray, is the most widely used imaging modality to detect cancer and other breast diseases. Recent studies have shown that deep learning-based computer-assis…
PediCXR: An open, large-scale chest radiograph dataset for interpretation of common thoracic diseases in children
Hieu H. Pham, Ngoc H. Nguyen, Thanh T. Tran +2
The development of diagnostic models for detecting and diagnosing pediatric diseases in CXR scans is undertaken due to the lack of high-quality physician-annotated datasets. To ove…
A Novel Transparency Strategy-based Data Augmentation Approach for BI-RADS Classification of Mammograms
Sam B. Tran, Huyen T. X. Nguyen, Chi Phan +2
Image augmentation techniques have been widely investigated to improve the performance of deep learning (DL) algorithms on mammography classification tasks. Recent methods have pro…
A novel multi-view deep learning approach for BI-RADS and density assessment of mammograms
Huyen T. X. Nguyen, Sam B. Tran, Dung B. Nguyen +2
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 standa…
DICOM Imaging Router: An Open Deep Learning Framework for Classification of Body Parts from DICOM X-ray Scans
Hieu H. Pham, Dung V. Do, Ha Q. Nguyen
X-ray imaging in DICOM format is the most commonly used imaging modality in clinical practice, resulting in vast, non-normalized databases. This leads to an obstacle in deploying A…
VinDr-RibCXR: A Benchmark Dataset for Automatic Segmentation and Labeling of Individual Ribs on Chest X-rays
Hoang C. Nguyen, Tung T. Le, Hieu H. Pham +1
We introduce a new benchmark dataset, namely VinDr-RibCXR, for automatic segmentation and labeling of individual ribs from chest X-ray (CXR) scans. The VinDr-RibCXR contains 245 CX…
VinDr-SpineXR: A deep learning framework for spinal lesions detection and classification from radiographs
Hieu T. Nguyen, Hieu H. Pham, Nghia T. Nguyen +4
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…
A clinical validation of VinDr-CXR, an AI system for detecting abnormal chest radiographs
Ngoc Huy Nguyen, Ha Quy Nguyen, Nghia Trung Nguyen +3
Computer-Aided Diagnosis (CAD) systems for chest radiographs using artificial intelligence (AI) have recently shown a great potential as a second opinion for radiologists. The perf…
VinDr-CXR: An open dataset of chest X-rays with radiologist's annotations
Ha Q. Nguyen, Khanh Lam, Linh T. Le +21
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 learn…
A CNN-LSTM Architecture for Detection of Intracranial Hemorrhage on CT scans
Nhan T. Nguyen, Dat Q. Tran, Nghia T. Nguyen +1
We propose a novel method that combines a convolutional neural network (CNN) with a long short-term memory (LSTM) mechanism for accurate prediction of intracranial hemorrhage on co…
Interpreting Chest X-rays via CNNs that Exploit Hierarchical Disease Dependencies and Uncertainty Labels
Hieu H. Pham, Tung T. Le, Dat T. Ngo +2
The chest X-rays (CXRs) is one of the views most commonly ordered by radiologists (NHS),which is critical for diagnosis of many different thoracic diseases. Accurately detecting th…
Semi-supervised Learning Approach to Generate Neuroimaging Modalities with Adversarial Training
Harrison Nguyen, Simon Luo, Fabio Ramos
Magnetic Resonance Imaging (MRI) of the brain can come in the form of different modalities such as T1-weighted and Fluid Attenuated Inversion Recovery (FLAIR) which has been used t…
Interpreting chest X-rays via CNNs that exploit hierarchical disease dependencies and uncertainty labels
Hieu H. Pham, Tung T. Le, Dat Q. Tran +2
Chest radiography is one of the most common types of diagnostic radiology exams, which is critical for screening and diagnosis of many different thoracic diseases. Specialized algo…
Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings
Pantelis Elinas, Edwin V. Bonilla, Louis Tiao
We propose a framework that lifts the capabilities of graph convolutional networks (GCNs) to scenarios where no input graph is given and increases their robustness to adversarial a…
Correcting differences in multi-site neuroimaging data using Generative Adversarial Networks
Harrison Nguyen, Richard W. Morris, Anthony W. Harris +2
Magnetic Resonance Imaging (MRI) of the brain has been used to investigate a wide range of neurological disorders, but data acquisition can be expensive, time-consuming, and inconv…
Top-Frequency Parallel Coordinates Plots
Vincent Yang, Harrison Nguyen, Norman Matloff +1
Parallel coordinates plotting is one of the most popular methods for multivariate visualization. However, when applied to larger data sets, there tends to be a "black screen proble…