Javier Civit-Masot
- Radiology, Nuclear Medicine and Imaging top 5%
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition top 5%
- Ophthalmology top 5%
- Biomedical Engineering
- Co-authors
- Manuel Domínguez-MoralesFrancisco Luna-PerejónSaturnino Vicente-DíazLuis Muñoz-SaavedraJosé María Rodríguez CorralMaría José EscalonaArturo Morgado‐EstévezShwetambara Malwade
- Topics
- AI in cancer detection (9 papers)Radiomics and Machine Learning in Medical Imaging (6 papers)Retinal Imaging and Analysis (4 papers)
- Cited by
- Health InformaticsRadiology, Nuclear Medicine and ImagingComputer Vision and Pattern Recognition
- Partner nations
- SpainUnited StatesGreece
In The Last Decade
Javier Civit-Masot
22 papers receiving 490 citations
Peers
Comparison fields: 5 of 98
- Radiology, Nuclear Medicine and Imaging 263
- Artificial Intelligence 195
- Computer Vision and Pattern Recognition 156
- Ophthalmology 65
- Biomedical Engineering 47
Countries citing papers authored by Javier Civit-Masot
This map shows the geographic impact of Javier Civit-Masot'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 Javier Civit-Masot with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Javier Civit-Masot more than expected).
Fields of papers citing papers by Javier Civit-Masot
This network shows the impact of papers produced by Javier Civit-Masot. 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 Javier Civit-Masot. The network helps show where Javier Civit-Masot may publish in the future.
Co-authorship network of co-authors of Javier Civit-Masot
This figure shows the co-authorship network connecting the top 25 collaborators of Javier Civit-Masot. A scholar is included among the top collaborators of Javier Civit-Masot 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 Javier Civit-Masot. Javier Civit-Masot is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 14 | |
| 2 | 11 | |
| 3 | 6 | |
| 4 | 17 | |
| 5 | 14 | |
| 6 | 8 | |
| 7 | 52 | |
| 8 | 61 | |
| 9 | 18 | |
| 10 | 6 | |
| 11 | 36 | |
| 12 | 70 | |
| 13 | 121 | |
| 14 | 13 | |
| 15 | 20 | |
| 16 | 29 | |
| 17 | 1 | |
| 18 | 2 | |
| 19 | 5 | |
| 20 | 3 |
About Javier Civit-Masot
Javier Civit-Masot is a scholar working on Health Informatics, Radiology, Nuclear Medicine and Imaging and Computer Vision and Pattern Recognition, having authored 22 papers that have together received 515 indexed citations. Recurring topics across this work include AI in cancer detection (9 papers), Radiomics and Machine Learning in Medical Imaging (6 papers) and Retinal Imaging and Analysis (4 papers). The work is most often cited by research in Health Informatics (41 citations), Radiology, Nuclear Medicine and Imaging (263 citations) and Computer Vision and Pattern Recognition (156 citations). Javier Civit-Masot has collaborated with scholars based in Spain, United States and Greece. Frequent co-authors include Manuel Domínguez-Morales, Francisco Luna-Perejón, Saturnino Vicente-Díaz, Luis Muñoz-Saavedra, José María Rodríguez Corral, María José Escalona, Arturo Morgado‐Estévez, Shwetambara Malwade, Yu‐Chuan Li and Evdokimos Konstantinidis. Their work appears in journals such as Expert Systems with Applications, IEEE Access and Sensors.
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.