1 citations · 2 across the 2 of their papers we have counts for
5 papers
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…
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…
Enhancing MRI Brain Tumor Segmentation with an Additional Classification Network
Hieu T. Nguyen, Tung T. Le, Thang V. Nguyen +1
Brain tumor segmentation plays an essential role in medical image analysis. In recent studies, deep convolution neural networks (DCNNs) are extremely powerful to tackle tumor segme…
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…
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…