Benjamin Chidester
Impact in
- Biophysics top 10%
- Cell Image Analysis Techniques
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- Advanced Vision and Imaging
Papers in
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- Single-cell and spatial transcriptomics 3
- Gene expression and cancer classification 2
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- Cell Image Analysis Techniques 3
- Co-authors
- Jian Ma (5 shared papers)N. Minh (7 shared papers)Tianming Zhou (2 shared papers)Sushant P. Sahu (1 shared paper)Manas Ranjan Gartia (1 shared paper)Georgios Veronis (1 shared paper)Minh–Triet Tran (1 shared paper)Hongsheng Yang (1 shared paper)
- Journals
- Nano Letters (1 paper)IEEE Transactions on Image Processing (1 paper)Nature Genetics (1 paper)Cell Reports Methods (1 paper)Bioinformatics (1 paper)
- Partner nations
- United StatesVietnamSingapore
In The Last Decade
Benjamin Chidester
11 papers receiving 165 citations
Peers
Comparison fields: 5 of 58
- Biophysics 35
- Computer Vision and Pattern Recognition 41
- Media Technology 13
- Acoustics and Ultrasonics 1
- Molecular Biology 65
Countries citing papers authored by Benjamin Chidester
This map shows the geographic impact of Benjamin Chidester'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 Chidester with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Benjamin Chidester more than expected).
Fields of papers citing papers by Benjamin Chidester
This network shows the impact of papers produced by Benjamin Chidester. 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 Chidester. The network helps show where Benjamin Chidester may publish in the future.
Co-authors
The 25 scholars most cited alongside Benjamin Chidester, 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 | 2023 | 49 | |
| 2 | 2019 | 29 | |
| 3 | 2019 | 21 | |
| 4 | 2019 | 20 | |
| 5 | 2014 | 17 | |
| 6 | 2017 | 7 | |
| 7 | 2013 | 6 | |
| 8 | 2014 | 5 | |
| 9 | 2024 | 4 | |
| 10 | 2018 | 4 | |
| 11 | 2017 | 4 |
About Benjamin Chidester
Benjamin Chidester is a scholar working on Molecular Biology, Biophysics, Computer Vision and Pattern Recognition, Artificial Intelligence and Media Technology, having authored 11 papers that have together received 166 indexed citations. Recurring topics across this work include Single-cell and spatial transcriptomics (3 papers), Cell Image Analysis Techniques (3 papers), Gene expression and cancer classification (2 papers), Advanced Vision and Imaging (2 papers), Image Processing Techniques and Applications (2 papers), AI in cancer detection (2 papers), Image Enhancement Techniques (1 paper) and Digital Imaging for Blood Diseases (1 paper). The work is most often cited by research in Biophysics (35 citations), Computer Vision and Pattern Recognition (41 citations), Media Technology (13 citations), Acoustics and Ultrasonics (1 citation) and Molecular Biology (65 citations). Benjamin Chidester has collaborated with scholars based in United States, Vietnam and Singapore. Frequent co-authors include Jian Ma, N. Minh, Tianming Zhou, Sushant P. Sahu, Manas Ranjan Gartia, Georgios Veronis, Minh–Triet Tran, Hongsheng Yang, Jiangbo Lu and Joanne Li. Their work appears in journals such as Nano Letters, IEEE Transactions on Image Processing, Nature Genetics, Cell Reports Methods and Bioinformatics.
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.