Matthew Thorpe
- Radiology, Nuclear Medicine and Imaging top 5%
- Artificial Intelligence top 5%
- Health Informatics top 0.5%
- Computer Vision and Pattern Recognition top 10%
- Biomedical Engineering
- Co-authors
- Dejan SlepčevSoheil KolouriGustavo K. RohdeEffrossyni Gkrania‐KlotsasCarola‐Bibiane SchönliebCathal McCagueJames H.F. RuddLucian Beer
- Topics
- Statistical Methods and Inference (4 papers)Topological and Geometric Data Analysis (4 papers)Sparse and Compressive Sensing Techniques (3 papers)
- Journals
- Pattern RecognitionIEEE Signal Processing MagazineArchive for Rational Mechanics and Analysis
- Partner nations
- United KingdomUnited StatesNetherlands
In The Last Decade
Matthew Thorpe
17 papers receiving 897 citations
Hit Papers
Peers
Comparison fields: 5 of 145
- Radiology, Nuclear Medicine and Imaging 380
- Artificial Intelligence 338
- Health Informatics 201
- Computer Vision and Pattern Recognition 106
- Biomedical Engineering 79
Countries citing papers authored by Matthew Thorpe
This map shows the geographic impact of Matthew Thorpe'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 Matthew Thorpe with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matthew Thorpe more than expected).
Fields of papers citing papers by Matthew Thorpe
This network shows the impact of papers produced by Matthew Thorpe. 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 Matthew Thorpe. The network helps show where Matthew Thorpe may publish in the future.
Co-authorship network of co-authors of Matthew Thorpe
This figure shows the co-authorship network connecting the top 25 collaborators of Matthew Thorpe. A scholar is included among the top collaborators of Matthew Thorpe 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 Matthew Thorpe. Matthew Thorpe 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 | 7 | |
| 3 | 1 | |
| 4 | 2 | |
| 5 | 2 | |
| 6 | 5 | |
| 7 | 16 | |
| 8 | 10 | |
| 9 | Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scansbreakdown → | 573 |
| 10 | 3 | |
| 11 | 15 | |
| 12 | 9 | |
| 13 | 6 | |
| 14 | 30 | |
| 15 | 227 | |
| 16 | 3 | |
| 17 | 8 | |
| 18 | Distinguishing between Humans and Robots on the Web | 4 |
About Matthew Thorpe
Matthew Thorpe is a scholar working on Health Informatics, Statistics and Probability and Applied Mathematics, having authored 18 papers that have together received 921 indexed citations. Recurring topics across this work include Statistical Methods and Inference (4 papers), Topological and Geometric Data Analysis (4 papers) and Sparse and Compressive Sensing Techniques (3 papers). The work is most often cited by research in Health Informatics (201 citations), Radiology, Nuclear Medicine and Imaging (380 citations) and Artificial Intelligence (338 citations). Matthew Thorpe has collaborated with scholars based in United Kingdom, United States and Netherlands. Frequent co-authors include Dejan Slepčev, Soheil Kolouri, Gustavo K. Rohde, Effrossyni Gkrania‐Klotsas, Carola‐Bibiane Schönlieb, Cathal McCague, James H.F. Rudd, Lucian Beer, Zhongzhao Teng and Derek Driggs. Their work appears in journals such as Pattern Recognition, IEEE Signal Processing Magazine and Archive for Rational Mechanics and 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.