Benjamin Q. Huynh
- Radiology, Nuclear Medicine and Imaging top 2%
- Artificial Intelligence top 2%
- Pulmonary and Respiratory Medicine
- Computer Vision and Pattern Recognition top 10%
- Biomedical Engineering
- Co-authors
- Maryellen L. GigerHui LiElizabeth T. ChinSanjay BasuKaren DrukkerNathan C. LoН. В. АнтроповаLloyd A. C. Chapman
- Topics
- Radiomics and Machine Learning in Medical Imaging (10 papers)AI in cancer detection (8 papers)MRI in cancer diagnosis (6 papers)
- Partner nations
- United StatesUnited KingdomAustralia
In The Last Decade
Benjamin Q. Huynh
18 papers receiving 1.1k citations
Hit Papers
Peers
Comparison fields: 5 of 127
- Radiology, Nuclear Medicine and Imaging 717
- Artificial Intelligence 648
- Pulmonary and Respiratory Medicine 155
- Computer Vision and Pattern Recognition 142
- Biomedical Engineering 116
Countries citing papers authored by Benjamin Q. Huynh
This map shows the geographic impact of Benjamin Q. Huynh'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 Benjamin Q. Huynh with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Benjamin Q. Huynh more than expected).
Fields of papers citing papers by Benjamin Q. Huynh
This network shows the impact of papers produced by Benjamin Q. Huynh. 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 Benjamin Q. Huynh. The network helps show where Benjamin Q. Huynh may publish in the future.
Co-authorship network of co-authors of Benjamin Q. Huynh
This figure shows the co-authorship network connecting the top 25 collaborators of Benjamin Q. Huynh. A scholar is included among the top collaborators of Benjamin Q. Huynh 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 Benjamin Q. Huynh. Benjamin Q. Huynh is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 6 | |
| 2 | 4 | |
| 3 | 0 | |
| 4 | 3 | |
| 5 | 0 | |
| 6 | 2 | |
| 7 | 50 | |
| 8 | 42 | |
| 9 | 61 | |
| 10 | 19 | |
| 11 | 28 | |
| 12 | 8 | |
| 13 | 27 | |
| 14 | 3 | |
| 15 | A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasetsbreakdown → | 298 |
| 16 | 11 | |
| 17 | 67 | |
| 18 | Digital mammographic tumor classification using transfer learning from deep convolutional neural networksbreakdown → | 393 |
| 19 | 23 | |
| 20 | 64 |
About Benjamin Q. Huynh
Benjamin Q. Huynh is a scholar working on Modeling and Simulation, Radiology, Nuclear Medicine and Imaging and Health, Toxicology and Mutagenesis, having authored 20 papers that have together received 1.1k indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (10 papers), AI in cancer detection (8 papers) and MRI in cancer diagnosis (6 papers). The work is most often cited by research in Health Informatics (54 citations), Radiology, Nuclear Medicine and Imaging (717 citations) and Artificial Intelligence (648 citations). Benjamin Q. Huynh has collaborated with scholars based in United States, United Kingdom and Australia. Frequent co-authors include Maryellen L. Giger, Hui Li, Elizabeth T. Chin, Sanjay Basu, Karen Drukker, Nathan C. Lo, Н. В. Антропова, Lloyd A. C. Chapman, Mathew V. Kiang and Matthew Murrill. Their work appears in journals such as Clinical Infectious Diseases, The Lancet Infectious Diseases and Medical Physics.
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.