James Monaco
Impact in
- Artificial Intelligence top 2%
- AI in cancer detection
- Health Informatics top 10%
- Artificial Intelligence in Healthcare and Education
Papers in
-
- AI in cancer detection 18
- Bayesian Modeling and Causal Inference 2
-
- Medical Image Segmentation Techniques 7
- Advanced Vision and Imaging 5
- Image Retrieval and Classification Techniques 2
- Co-authors
- Anant Madabhushi (23 shared papers)Michael D. Feldman (9 shared papers)John Tomaszewski (10 shared papers)Ajay N. Basavanhally (3 shared papers)Gyan Bhanot (2 shared papers)Shridar Ganesan (2 shared papers)Shannon C. Agner (1 shared paper)J E Tomaszewski (1 shared paper)
- Journals
- Journal of Pathology Informatics (5 papers)Medical Image Analysis (2 papers)Analytical Cellular Pathology (2 papers)BMC Bioinformatics (1 paper)International Journal of Computer Vision (1 paper)
- Partner nations
- United StatesCanadaGermany
In The Last Decade
James Monaco
30 papers receiving 777 citations
Peers
Comparison fields: 5 of 58
- Artificial Intelligence 543
- Health Informatics 24
- Computer Vision and Pattern Recognition 151
- Biophysics 43
- Radiology, Nuclear Medicine and Imaging 86
Countries citing papers authored by James Monaco
This map shows the geographic impact of James Monaco'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 James Monaco with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites James Monaco more than expected).
Fields of papers citing papers by James Monaco
This network shows the impact of papers produced by James Monaco. 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 James Monaco. The network helps show where James Monaco may publish in the future.
Co-authors
The 25 scholars most cited alongside James Monaco, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 30 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2009 | 229 | |
| 2 | 2010 | 99 | |
| 3 | 2011 | 63 | |
| 4 | 2011 | 57 | |
| 5 | 2009 | 36 | |
| 6 | 2010 | 34 | |
| 7 | 2009 | 30 | |
| 8 | 2012 | 26 | |
| 9 | 2012 | 24 | |
| 10 | 2010 | 22 | |
| 11 | 2011 | 16 | |
| 12 | 2011 | 15 | |
| 13 | 2012 | 14 | |
| 14 | 2012 | 13 | |
| 15 | 2009 | 12 | |
| 16 | 2010 | 12 | |
| 17 | 2018 | 12 | |
| 18 | 2002 | 11 | |
| 19 | 2008 | 11 | |
| 20 | 2011 | 9 |
About James Monaco
James Monaco is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Biophysics, Health Informatics and Statistics and Probability, having authored 30 papers that have together received 792 indexed citations. Recurring topics across this work include AI in cancer detection (18 papers), Medical Image Segmentation Techniques (7 papers), Advanced Vision and Imaging (5 papers), Cell Image Analysis Techniques (2 papers), Bayesian Modeling and Causal Inference (2 papers), Image Retrieval and Classification Techniques (2 papers), Color Science and Applications (1 paper) and Research Data Management Practices (1 paper). The work is most often cited by research in Artificial Intelligence (543 citations), Health Informatics (24 citations), Computer Vision and Pattern Recognition (151 citations), Biophysics (43 citations) and Radiology, Nuclear Medicine and Imaging (86 citations). James Monaco has collaborated with scholars based in United States, Canada and Germany. Frequent co-authors include Anant Madabhushi, Michael D. Feldman, John Tomaszewski, Ajay N. Basavanhally, Gyan Bhanot, Shridar Ganesan, Shannon C. Agner, J E Tomaszewski, Scott T. Doyle and Ulysses J. Balis. Their work appears in journals such as Journal of Pathology Informatics, Medical Image Analysis, Analytical Cellular Pathology, BMC Bioinformatics and International Journal of Computer Vision.
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.