Roberto Vega
- Radiology, Nuclear Medicine and Imaging top 10%
- Ophthalmology top 5%
- Computer Vision and Pattern Recognition top 10%
- Artificial Intelligence
- Cognitive Neuroscience
- Co-authors
- Gildardo Sanchez‐AnteLuis E. Falcón-MoralesHumberto SossaElizabeth Guevara‐GutiérrezRussell GreinerBin ZhengJacob L. JaremkoEsther Fujiwara
- Topics
- COVID-19 epidemiological studies (2 papers)Digital Imaging for Blood Diseases (2 papers)AI in cancer detection (2 papers)
- Journals
- SHILAP Revista de lepidopterologíaScientific ReportsThe American Journal of Surgery
- Partner nations
- CanadaUnited StatesMexico
In The Last Decade
Roberto Vega
15 papers receiving 233 citations
Peers
Comparison fields: 5 of 73
- Radiology, Nuclear Medicine and Imaging 114
- Ophthalmology 76
- Computer Vision and Pattern Recognition 71
- Artificial Intelligence 41
- Cognitive Neuroscience 35
Countries citing papers authored by Roberto Vega
This map shows the geographic impact of Roberto Vega'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 Roberto Vega with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Roberto Vega more than expected).
Fields of papers citing papers by Roberto Vega
This network shows the impact of papers produced by Roberto Vega. 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 Roberto Vega. The network helps show where Roberto Vega may publish in the future.
Co-authorship network of co-authors of Roberto Vega
This figure shows the co-authorship network connecting the top 25 collaborators of Roberto Vega. A scholar is included among the top collaborators of Roberto Vega 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 Roberto Vega. Roberto Vega is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 7 | |
| 3 | 6 | |
| 4 | 21 | |
| 5 | Sample efficient learning of image-based diagnostic classifiers via probabilistic labels | 3 |
| 6 | 18 | |
| 7 | 6 | |
| 8 | 4 | |
| 9 | 13 | |
| 10 | 17 | |
| 11 | 7 | |
| 12 | 7 | |
| 13 | 19 | |
| 14 | 15 | |
| 15 | 99 |
About Roberto Vega
Roberto Vega is a scholar working on Health Informatics, Modeling and Simulation and Critical Care and Intensive Care Medicine, having authored 15 papers that have together received 243 indexed citations. Recurring topics across this work include COVID-19 epidemiological studies (2 papers), Digital Imaging for Blood Diseases (2 papers) and AI in cancer detection (2 papers). The work is most often cited by research in Ophthalmology (76 citations), Health Informatics (11 citations) and Modeling and Simulation (29 citations). Roberto Vega has collaborated with scholars based in Canada, United States and Mexico. Frequent co-authors include Gildardo Sanchez‐Ante, Luis E. Falcón-Morales, Humberto Sossa, Elizabeth Guevara‐Gutiérrez, Russell Greiner, Bin Zheng, Jacob L. Jaremko, Esther Fujiwara, Xiaohong Ma and Xiangzhen Kong. Their work appears in journals such as SHILAP Revista de lepidopterología, Scientific Reports and The American Journal of Surgery.
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.