Quoc Dang Vu
- Artificial Intelligence top 10%
- Radiology, Nuclear Medicine and Imaging top 10%
- Computer Vision and Pattern Recognition top 10%
- Biomedical Engineering
- Pulmonary and Respiratory Medicine
- Co-authors
- Nasir RajpootShan E Ahmed RazaJin Tae KwakSimon GrahamFayyaz MinhasMostafa JahanifarDavid SneadMinh Nguyen Nhat To
- Topics
- AI in cancer detection (8 papers)Radiomics and Machine Learning in Medical Imaging (5 papers)Cell Image Analysis Techniques (4 papers)
- Journals
- Journal of Clinical OncologySHILAP Revista de lepidopterologíaMedical Image Analysis
- Partner nations
- United KingdomSouth KoreaUnited States
In The Last Decade
Quoc Dang Vu
11 papers receiving 247 citations
Peers
Comparison fields: 5 of 40
- Artificial Intelligence 166
- Radiology, Nuclear Medicine and Imaging 130
- Computer Vision and Pattern Recognition 99
- Biomedical Engineering 38
- Pulmonary and Respiratory Medicine 35
Countries citing papers authored by Quoc Dang Vu
This map shows the geographic impact of Quoc Dang Vu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Quoc Dang Vu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Quoc Dang Vu more than expected).
Fields of papers citing papers by Quoc Dang Vu
This network shows the impact of papers produced by Quoc Dang Vu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Quoc Dang Vu. The network helps show where Quoc Dang Vu may publish in the future.
Co-authorship network of co-authors of Quoc Dang Vu
This figure shows the co-authorship network connecting the top 25 collaborators of Quoc Dang Vu. A scholar is included among the top collaborators of Quoc Dang Vu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Quoc Dang Vu. Quoc Dang Vu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 11 | |
| 2 | 26 | |
| 3 | 52 | |
| 4 | 75 | |
| 5 | 1 | |
| 6 | 7 | |
| 7 | 13 | |
| 8 | 4 | |
| 9 | 52 | |
| 10 | XY Network for Nuclear Segmentation in Multi-Tissue Histology Images. | 6 |
| 11 | 2 |
About Quoc Dang Vu
Quoc Dang Vu is a scholar working on Biophysics, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 11 papers that have together received 249 indexed citations. Recurring topics across this work include AI in cancer detection (8 papers), Radiomics and Machine Learning in Medical Imaging (5 papers) and Cell Image Analysis Techniques (4 papers). The work is most often cited by research in Health Informatics (10 citations), Biophysics (35 citations) and Radiology, Nuclear Medicine and Imaging (130 citations). Quoc Dang Vu has collaborated with scholars based in United Kingdom, South Korea and United States. Frequent co-authors include Nasir Rajpoot, Shan E Ahmed Raza, Jin Tae Kwak, Simon Graham, Fayyaz Minhas, Mostafa Jahanifar, David Snead, Minh Nguyen Nhat To, Barış Türkbey and Peter L. Choyke. Their work appears in journals such as Journal of Clinical Oncology, SHILAP Revista de lepidopterología and Medical Image Analysis.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.