William H. Nailon
- Computer Vision and Pattern Recognition top 5%
- Radiology, Nuclear Medicine and Imaging top 10%
- Molecular Biology
- Biomedical Engineering
- Artificial Intelligence top 10%
- Co-authors
- Stephen McLaughlinTimothy R. SpencerM.P. RamoDuncan B. McLarenJames W. IronsideDiane RitchieMatthew BishopMike E. Davies
- Topics
- Radiomics and Machine Learning in Medical Imaging (13 papers)Advanced Radiotherapy Techniques (8 papers)Medical Imaging Techniques and Applications (7 papers)
- Partner nations
- United KingdomUnited StatesNetherlands
In The Last Decade
William H. Nailon
35 papers receiving 752 citations
Peers
Comparison fields: 5 of 127
- Computer Vision and Pattern Recognition 239
- Radiology, Nuclear Medicine and Imaging 195
- Molecular Biology 179
- Biomedical Engineering 143
- Artificial Intelligence 94
Countries citing papers authored by William H. Nailon
This map shows the geographic impact of William H. Nailon'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 William H. Nailon with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites William H. Nailon more than expected).
Fields of papers citing papers by William H. Nailon
This network shows the impact of papers produced by William H. Nailon. 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 William H. Nailon. The network helps show where William H. Nailon may publish in the future.
Co-authorship network of co-authors of William H. Nailon
This figure shows the co-authorship network connecting the top 25 collaborators of William H. Nailon. A scholar is included among the top collaborators of William H. Nailon 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 William H. Nailon. William H. Nailon is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 6 | |
| 3 | 3 | |
| 4 | 16 | |
| 5 | 0 | |
| 6 | 7 | |
| 7 | 5 | |
| 8 | 58 | |
| 9 | 17 | |
| 10 | 4 | |
| 11 | 4 | |
| 12 | 37 | |
| 13 | 2 | |
| 14 | 25 | |
| 15 | 3 | |
| 16 | 11 | |
| 17 | Real-time combustion knock processing using a single instruction multiple data automotive PowerPC system-on-a-chip | 6 |
| 18 | 133 | |
| 19 | 11 | |
| 20 | Can statistical texture analysis of unprocessed intravascular ultrasound (IVUS) signal discriminate red and white thrombi acid plasma | 1 |
About William H. Nailon
William H. Nailon is a scholar working on Radiology, Nuclear Medicine and Imaging, Radiation and Health Informatics, having authored 38 papers that have together received 782 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (13 papers), Advanced Radiotherapy Techniques (8 papers) and Medical Imaging Techniques and Applications (7 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (239 citations), Neurology (77 citations) and Radiology, Nuclear Medicine and Imaging (195 citations). William H. Nailon has collaborated with scholars based in United Kingdom, United States and Netherlands. Frequent co-authors include Stephen McLaughlin, Timothy R. Spencer, M.P. Ramo, Duncan B. McLaren, James W. Ironside, Diane Ritchie, Matthew Bishop, Mike E. Davies, Linda McCardle and Mark Head. Their work appears in journals such as Circulation, PLoS ONE and Analytical Chemistry.
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.