Leonardo S. Mattos
- Biomedical Engineering top 5%
- Surgery top 10%
- Computer Vision and Pattern Recognition top 2%
- Radiology, Nuclear Medicine and Imaging top 5%
- Pulmonary and Respiratory Medicine top 10%
- Co-authors
- Elena De MomiSara MocciaDarwin G. CaldwellSara El HadjiGiacinto BarresiGiorgio PerettiNikhil DeshpandeLuca Guastini
- Topics
- Surgical Simulation and Training (47 papers)Soft Robotics and Applications (35 papers)Anatomy and Medical Technology (21 papers)
- Partner nations
- ItalyUnited StatesUnited Kingdom
In The Last Decade
Leonardo S. Mattos
150 papers receiving 2.1k citations
Hit Papers
Peers
Comparison fields: 5 of 149
- Biomedical Engineering 819
- Surgery 508
- Computer Vision and Pattern Recognition 480
- Radiology, Nuclear Medicine and Imaging 458
- Pulmonary and Respiratory Medicine 247
Countries citing papers authored by Leonardo S. Mattos
This map shows the geographic impact of Leonardo S. Mattos'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 Leonardo S. Mattos with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Leonardo S. Mattos more than expected).
Fields of papers citing papers by Leonardo S. Mattos
This network shows the impact of papers produced by Leonardo S. Mattos. 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 Leonardo S. Mattos. The network helps show where Leonardo S. Mattos may publish in the future.
Co-authorship network of co-authors of Leonardo S. Mattos
This figure shows the co-authorship network connecting the top 25 collaborators of Leonardo S. Mattos. A scholar is included among the top collaborators of Leonardo S. Mattos 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 Leonardo S. Mattos. Leonardo S. Mattos 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 | 12 | |
| 3 | 0 | |
| 4 | 4 | |
| 5 | 4 | |
| 6 | 6 | |
| 7 | 27 | |
| 8 | 21 | |
| 9 | 9 | |
| 10 | 11 | |
| 11 | 73 | |
| 12 | 31 | |
| 13 | 28 | |
| 14 | 11 | |
| 15 | 22 | |
| 16 | FCNN-based segmentation of kidney vessels - Towards constraints definition for safe robot-assisted nephrectomy | 1 |
| 17 | 12 | |
| 18 | 13 | |
| 19 | 1 | |
| 20 | Precision localization in monte carlo sensor networks | 3 |
About Leonardo S. Mattos
Leonardo S. Mattos is a scholar working on Otorhinolaryngology, Biomedical Engineering and Computer Vision and Pattern Recognition, having authored 155 papers that have together received 2.2k indexed citations. Recurring topics across this work include Surgical Simulation and Training (47 papers), Soft Robotics and Applications (35 papers) and Anatomy and Medical Technology (21 papers). The work is most often cited by research in Health Informatics (68 citations), Otorhinolaryngology (151 citations) and Computer Vision and Pattern Recognition (480 citations). Leonardo S. Mattos has collaborated with scholars based in Italy, United States and United Kingdom. Frequent co-authors include Elena De Momi, Sara Moccia, Darwin G. Caldwell, Sara El Hadji, Giacinto Barresi, Giorgio Peretti, Nikhil Deshpande, Luca Guastini, Edward Grant and Zhuoqi Cheng. Their work appears in journals such as Advanced Materials, Annals of Internal Medicine and Scientific Reports.
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.