Nicholas Trahearn
Impact in
- Biophysics top 10%
- Cell Image Analysis Techniques
- Modeling and Simulation top 10%
- Mathematical Biology Tumor Growth
Papers in
-
- AI in cancer detection 5
-
- Medical Image Segmentation Techniques 3
- Digital Imaging for Blood Diseases 2
- Co-authors
- Nasir Rajpoot (6 shared papers)David Snead (5 shared papers)Najah Alsubaie (2 shared papers)Shan E Ahmed Raza (2 shared papers)Ian A. Cree (4 shared papers)Andrea Sottoriva (3 shared papers)Carlo C. Maley (2 shared papers)Iros Barozzi (1 shared paper)
- Journals
- Nature Communications (1 paper)Scientific Reports (1 paper)PLoS ONE (1 paper)EBioMedicine (1 paper)Cytometry Part A (1 paper)
- Partner nations
- United KingdomUnited StatesQatar
In The Last Decade
Nicholas Trahearn
10 papers receiving 193 citations
Peers
Comparison fields: 5 of 42
- Biophysics 42
- Modeling and Simulation 20
- Cancer Research 44
- Computer Vision and Pattern Recognition 63
- Artificial Intelligence 88
Countries citing papers authored by Nicholas Trahearn
This map shows the geographic impact of Nicholas Trahearn'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 Nicholas Trahearn with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nicholas Trahearn more than expected).
Fields of papers citing papers by Nicholas Trahearn
This network shows the impact of papers produced by Nicholas Trahearn. 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 Nicholas Trahearn. The network helps show where Nicholas Trahearn may publish in the future.
Co-authors
The 25 scholars most cited alongside Nicholas Trahearn, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 76 | |
| 2 | 2017 | 58 | |
| 3 | 2015 | 21 | |
| 4 | 2017 | 17 | |
| 5 | 2016 | 7 | |
| 6 | 2019 | 7 | |
| 7 | 2014 | 4 | |
| 8 | A Discriminative Framework for Stain Deconvolution of Histopathology Images in the Maxwellian Space | 2015 | 2 |
| 9 | 2025 | 2 | |
| 10 | Automated Quantification Of Blood Microvessels In Hematoxylin And Eosin Whole Slide Images | 2021 | 1 |
About Nicholas Trahearn
Nicholas Trahearn is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Molecular Biology, Radiology, Nuclear Medicine and Imaging and Cancer Research, having authored 10 papers that have together received 195 indexed citations. Recurring topics across this work include AI in cancer detection (5 papers), Medical Image Segmentation Techniques (3 papers), Cancer Genomics and Diagnostics (2 papers), Digital Imaging for Blood Diseases (2 papers), Radiomics and Machine Learning in Medical Imaging (1 paper), Angiogenesis and VEGF in Cancer (1 paper), Gene expression and cancer classification (1 paper) and Medical Imaging and Analysis (1 paper). The work is most often cited by research in Biophysics (42 citations), Modeling and Simulation (20 citations), Cancer Research (44 citations), Computer Vision and Pattern Recognition (63 citations) and Artificial Intelligence (88 citations). Nicholas Trahearn has collaborated with scholars based in United Kingdom, United States and Qatar. Frequent co-authors include Nasir Rajpoot, David Snead, Najah Alsubaie, Shan E Ahmed Raza, Ian A. Cree, Andrea Sottoriva, Carlo C. Maley, Iros Barozzi, Ahmet Acar and Nicola Valeri. Their work appears in journals such as Nature Communications, Scientific Reports, PLoS ONE, EBioMedicine and Cytometry Part A.
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.