Brian Helba
Impact in
- Health Informatics top 5%
- Artificial Intelligence in Healthcare and Education
- Oncology top 5%
- Cutaneous Melanoma Detection and Management
Papers in
- Oncology 7
- Cutaneous Melanoma Detection and Management 7
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- AI in cancer detection 6
- Co-authors
- Noel Codella (6 shared papers)Allan C. Halpern (6 shared papers)David Gutman (6 shared papers)Michael A. Marchetti (4 shared papers)Stephen W. Dusza (4 shared papers)Aadi Kalloo (3 shared papers)M. Emre Celebi (3 shared papers)Konstantinos Liopyris (4 shared papers)
- Journals
- Journal of the American Academy of Dermatology (2 papers)The Lancet Digital Health (1 paper)Scientific Data (1 paper)mSphere (1 paper)Proceedings of the National Academy of Sciences (1 paper)
- Partner nations
- United StatesSpainAustria
In The Last Decade
Brian Helba
11 papers receiving 655 citations
Peers
Comparison fields: 5 of 91
- Health Informatics 44
- Oncology 477
- Biophysics 70
- Artificial Intelligence 371
- Dermatology 75
Countries citing papers authored by Brian Helba
This map shows the geographic impact of Brian Helba'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 Brian Helba with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Brian Helba more than expected).
Fields of papers citing papers by Brian Helba
This network shows the impact of papers produced by Brian Helba. 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 Brian Helba. The network helps show where Brian Helba may publish in the future.
Co-authors
The 25 scholars most cited alongside Brian Helba, 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 | 2017 | 212 | |
| 2 | 2018 | 125 | |
| 3 | 2021 | 94 | |
| 4 | 2022 | 74 | |
| 5 | 2019 | 63 | |
| 6 | 2021 | 48 | |
| 7 | 2024 | 36 | |
| 8 | 2016 | 19 | |
| 9 | 2022 | 6 | |
| 10 | 2018 | 2 | |
| 11 | 2016 | 2 |
About Brian Helba
Brian Helba is a scholar working on Oncology, Artificial Intelligence, Epidemiology, Biomedical Engineering and Radiology, Nuclear Medicine and Imaging, having authored 11 papers that have together received 681 indexed citations. Recurring topics across this work include Cutaneous Melanoma Detection and Management (7 papers), AI in cancer detection (6 papers), Nonmelanoma Skin Cancer Studies (4 papers), Medical Imaging Techniques and Applications (2 papers), Optical Coherence Tomography Applications (1 paper), Cell Image Analysis Techniques (1 paper), Trace Elements in Health (1 paper) and Advanced Radiotherapy Techniques (1 paper). The work is most often cited by research in Health Informatics (44 citations), Oncology (477 citations), Biophysics (70 citations), Artificial Intelligence (371 citations) and Dermatology (75 citations). Brian Helba has collaborated with scholars based in United States, Spain and Austria. Frequent co-authors include Noel Codella, Allan C. Halpern, David Gutman, Michael A. Marchetti, Stephen W. Dusza, Aadi Kalloo, M. Emre Celebi, Konstantinos Liopyris, Nabin K. Mishra and Harald Kittler. Their work appears in journals such as Journal of the American Academy of Dermatology, The Lancet Digital Health, Scientific Data, mSphere and Proceedings of the National Academy of Sciences.
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.