6 citations · 15 across the 8 of their papers we have counts for
12 papers
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…
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…
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…