Mike Feldman
Impact in
- Artificial Intelligence top 5%
- AI in cancer detection
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- Digital Imaging for Blood Diseases
- Medical Image Segmentation Techniques
Papers in
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- Digital Imaging for Blood Diseases 2
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- AI in cancer detection 2
- Co-authors
- Natalie Shih (2 shared papers)Anant Madabhushi (2 shared papers)Hannah Gilmore (2 shared papers)Ajay Basavanhally (2 shared papers)Fabio A. González (2 shared papers)John Tomaszewski (2 shared papers)Haibo Wang (2 shared papers)Ángel Cruz-Roa (2 shared papers)
- Journals
- Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease (1 paper)Journal of Vascular Research (1 paper)Journal of Medical Imaging (1 paper)Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE (1 paper)
- Partner nations
- United StatesColombiaJapan
In The Last Decade
Mike Feldman
4 papers receiving 392 citations
Peers
Comparison fields: 5 of 76
- Artificial Intelligence 257
- Computer Vision and Pattern Recognition 159
- Radiology, Nuclear Medicine and Imaging 146
- Biophysics 37
- Health Informatics 8
Countries citing papers authored by Mike Feldman
This map shows the geographic impact of Mike Feldman'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 Mike Feldman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mike Feldman more than expected).
Fields of papers citing papers by Mike Feldman
This network shows the impact of papers produced by Mike Feldman. 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 Mike Feldman. The network helps show where Mike Feldman may publish in the future.
Co-authors
The 25 scholars most cited alongside Mike Feldman, 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 | 2014 | 252 | |
| 2 | 2014 | 83 | |
| 3 | 2018 | 58 | |
| 4 | 1999 | 10 |
About Mike Feldman
Mike Feldman is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Molecular Biology and Cancer Research, having authored 4 papers that have together received 403 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (2 papers), AI in cancer detection (2 papers), Digital Imaging for Blood Diseases (2 papers), RNA modifications and cancer (1 paper), Cancer, Hypoxia, and Metabolism (1 paper) and Mitochondrial Function and Pathology (1 paper). The work is most often cited by research in Artificial Intelligence (257 citations), Computer Vision and Pattern Recognition (159 citations), Radiology, Nuclear Medicine and Imaging (146 citations), Biophysics (37 citations) and Health Informatics (8 citations). Mike Feldman has collaborated with scholars based in United States, Colombia and Japan. Frequent co-authors include Natalie Shih, Anant Madabhushi, Hannah Gilmore, Ajay Basavanhally, Fabio A. González, John Tomaszewski, Haibo Wang, Ángel Cruz-Roa, Satish Srinivasan and Martin Picard. Their work appears in journals such as Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease, Journal of Vascular Research, Journal of Medical Imaging and Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE.
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.