296 citations
- Hanoi University of Science and TechnologyVN6 papers
- Hong Kong University of Science and TechnologyHK3 papers
- Monash UniversityAU3 papers
- National University of SingaporeSG3 papers
- University of AizuJP3 papers
- University of Illinois Urbana-ChampaignUS3 papers
- University of LuxembourgLU3 papers
- Vietnam National University Ho Chi Minh CityVN3 papers
- Agency for Science, Technology and ResearchSG2 papers
- Can Tho UniversityVN2 papers
- Deakin UniversityAU2 papers
- Fulbright University VietnamVN2 papers
8 papers · 1 filter
Graph-Theoretic Consistency for Robust and Topology-Aware Semi-Supervised Histopathology Segmentation
Ha-Hieu Pham, Minh Le, Han Huynh +2
Semi-supervised semantic segmentation (SSSS) is vital in computational pathology, where dense annotations are costly and limited. Existing methods often rely on pixel-level consist…
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…
Detecting COVID-19 from digitized ECG printouts using 1D convolutional neural networks
Thao Nguyen, Hieu H. Pham, Huy Khiem Le +3
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 diag…
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…
SegTransVAE: Hybrid CNN -- Transformer with Regularization for medical image segmentation
Quan-Dung Pham, Hai Nguyen-Truong, Nam Nguyen Phuong +1
Current research on deep learning for medical image segmentation exposes their limitations in learning either global semantic information or local contextual information. To tackle…
The Federated Tumor Segmentation (FeTS) Challenge
Sarthak Pati, Ujjwal Baid, Maximilian Zenk +29
This manuscript describes the first challenge on Federated Learning, namely the Federated Tumor Segmentation (FeTS) challenge 2021. International challenges have become the standar…