Marcos Salganicoff
- Radiology, Nuclear Medicine and Imaging top 2%
- Pulmonary and Respiratory Medicine top 5%
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 5%
- Biomedical Engineering
- Co-authors
- Arun V. KrishnanLe LüM. Ali Akber DewanToshiro KubotaGeorge L. GersteinAnna JerebkoMatthias WolfJinbo Bi
- Topics
- Radiomics and Machine Learning in Medical Imaging (14 papers)Lung Cancer Diagnosis and Treatment (11 papers)Robot Manipulation and Learning (9 papers)
- Partner nations
- United StatesGermanyUnited Kingdom
In The Last Decade
Marcos Salganicoff
54 papers receiving 1.2k citations
Peers
Comparison fields: 5 of 97
- Radiology, Nuclear Medicine and Imaging 577
- Pulmonary and Respiratory Medicine 426
- Artificial Intelligence 331
- Computer Vision and Pattern Recognition 247
- Biomedical Engineering 210
Countries citing papers authored by Marcos Salganicoff
This map shows the geographic impact of Marcos Salganicoff'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 Marcos Salganicoff with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marcos Salganicoff more than expected).
Fields of papers citing papers by Marcos Salganicoff
This network shows the impact of papers produced by Marcos Salganicoff. 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 Marcos Salganicoff. The network helps show where Marcos Salganicoff may publish in the future.
Co-authorship network of co-authors of Marcos Salganicoff
This figure shows the co-authorship network connecting the top 25 collaborators of Marcos Salganicoff. A scholar is included among the top collaborators of Marcos Salganicoff based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Marcos Salganicoff. Marcos Salganicoff is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 15 | |
| 2 | 5 | |
| 3 | 46 | |
| 4 | 31 | |
| 5 | 8 | |
| 6 | 17 | |
| 7 | 24 | |
| 8 | 63 | |
| 9 | 143 | |
| 10 | 40 | |
| 11 | 43 | |
| 12 | 34 | |
| 13 | 42 | |
| 14 | 34 | |
| 15 | 9 | |
| 16 | 2 | |
| 17 | 1 | |
| 18 | Sensorimotor Learning Using Active Perception in Continuous Domains | 4 |
| 19 | 36 | |
| 20 | 84 |
About Marcos Salganicoff
Marcos Salganicoff is a scholar working on Internal Medicine, Radiology, Nuclear Medicine and Imaging and Artificial Intelligence, having authored 56 papers that have together received 1.2k indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (14 papers), Lung Cancer Diagnosis and Treatment (11 papers) and Robot Manipulation and Learning (9 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (577 citations), Internal Medicine (85 citations) and Pulmonary and Respiratory Medicine (426 citations). Marcos Salganicoff has collaborated with scholars based in United States, Germany and United Kingdom. Frequent co-authors include Arun V. Krishnan, Le Lü, M. Ali Akber Dewan, Toshiro Kubota, George L. Gerstein, Anna Jerebko, Matthias Wolf, Jinbo Bi, Joachim E. Wildberger and Marco Das. Their work appears in journals such as IEEE Transactions on Medical Imaging, American Journal of Roentgenology and Computer.
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.