Vincenzo L’Imperio
- Molecular Biology
- Artificial Intelligence top 5%
- Radiology, Nuclear Medicine and Imaging top 5%
- Pulmonary and Respiratory Medicine
- Oncology
- Co-authors
- Fabio PagniAndrew SmithFulvio MagniFilippo FraggettaClizia ChinelloFederico PieruzziAlessandro CaputoFrancesca Bono
- Topics
- Renal Diseases and Glomerulopathies (22 papers)Radiomics and Machine Learning in Medical Imaging (16 papers)AI in cancer detection (16 papers)
- Journals
- SHILAP Revista de lepidopterologíaScientific ReportsInternational Journal of Molecular Sciences
- Partner nations
- ItalyUnited StatesGermany
In The Last Decade
Vincenzo L’Imperio
88 papers receiving 944 citations
Peers
Comparison fields: 5 of 87
- Molecular Biology 245
- Artificial Intelligence 245
- Radiology, Nuclear Medicine and Imaging 241
- Pulmonary and Respiratory Medicine 184
- Oncology 175
Countries citing papers authored by Vincenzo L’Imperio
This map shows the geographic impact of Vincenzo L’Imperio'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 Vincenzo L’Imperio with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Vincenzo L’Imperio more than expected).
Fields of papers citing papers by Vincenzo L’Imperio
This network shows the impact of papers produced by Vincenzo L’Imperio. 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 Vincenzo L’Imperio. The network helps show where Vincenzo L’Imperio may publish in the future.
Co-authorship network of co-authors of Vincenzo L’Imperio
This figure shows the co-authorship network connecting the top 25 collaborators of Vincenzo L’Imperio. A scholar is included among the top collaborators of Vincenzo L’Imperio 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 Vincenzo L’Imperio. Vincenzo L’Imperio is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 0 | |
| 4 | 0 | |
| 5 | 2 | |
| 6 | 2 | |
| 7 | 0 | |
| 8 | 7 | |
| 9 | 1 | |
| 10 | 30 | |
| 11 | 4 | |
| 12 | 12 | |
| 13 | 8 | |
| 14 | 27 | |
| 15 | 3 | |
| 16 | 5 | |
| 17 | 13 | |
| 18 | 2 | |
| 19 | 16 | |
| 20 | 19 |
About Vincenzo L’Imperio
Vincenzo L’Imperio is a scholar working on Nephrology, Health Informatics and Transplantation, having authored 103 papers that have together received 953 indexed citations. Recurring topics across this work include Renal Diseases and Glomerulopathies (22 papers), Radiomics and Machine Learning in Medical Imaging (16 papers) and AI in cancer detection (16 papers). The work is most often cited by research in Health Informatics (62 citations), Nephrology (150 citations) and Radiology, Nuclear Medicine and Imaging (241 citations). Vincenzo L’Imperio has collaborated with scholars based in Italy, United States and Germany. Frequent co-authors include Fabio Pagni, Andrew Smith, Fulvio Magni, Filippo Fraggetta, Clizia Chinello, Federico Pieruzzi, Alessandro Caputo, Francesca Bono, Franco Ferrario and Albino Eccher. Their work appears in journals such as SHILAP Revista de lepidopterología, Scientific Reports and International Journal of Molecular 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.