Miguel Souto
- Radiology, Nuclear Medicine and Imaging top 5%
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 2%
- Pulmonary and Respiratory Medicine top 10%
- Biomedical Engineering
- Co-authors
- Pablo G. TahocesJ VidalMarı́a J. LadoArturo J. MéndezManuel G. PenedoCelia VarelaJosé M. CarreiraWilliam A. Pearlman
- Topics
- Radiomics and Machine Learning in Medical Imaging (18 papers)Advanced X-ray and CT Imaging (14 papers)Digital Radiography and Breast Imaging (11 papers)
- Cited by
- Computer Vision and Pattern RecognitionRadiology, Nuclear Medicine and ImagingHealth Informatics
- Journals
- SHILAP Revista de lepidopterologíaRadiologyIEEE Transactions on Medical Imaging
- Partner nations
- SpainUnited StatesFrance
In The Last Decade
Miguel Souto
53 papers receiving 942 citations
Peers
Comparison fields: 5 of 96
- Radiology, Nuclear Medicine and Imaging 455
- Artificial Intelligence 435
- Computer Vision and Pattern Recognition 430
- Pulmonary and Respiratory Medicine 328
- Biomedical Engineering 109
Countries citing papers authored by Miguel Souto
This map shows the geographic impact of Miguel Souto'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 Miguel Souto with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Miguel Souto more than expected).
Fields of papers citing papers by Miguel Souto
This network shows the impact of papers produced by Miguel Souto. 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 Miguel Souto. The network helps show where Miguel Souto may publish in the future.
Co-authorship network of co-authors of Miguel Souto
This figure shows the co-authorship network connecting the top 25 collaborators of Miguel Souto. A scholar is included among the top collaborators of Miguel Souto 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 Miguel Souto. Miguel Souto 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 | 2 | |
| 3 | 24 | |
| 4 | 12 | |
| 5 | 16 | |
| 6 | 2 | |
| 7 | 23 | |
| 8 | 2 | |
| 9 | Semiautomatic quantification of left and right ventricular functions in magnetic resonance imaging | 2 |
| 10 | 4 | |
| 11 | 43 | |
| 12 | 97 | |
| 13 | 33 | |
| 14 | 15 | |
| 15 | 5 | |
| 16 | 101 | |
| 17 | 16 | |
| 18 | 51 | |
| 19 | 26 | |
| 20 | 10 |
About Miguel Souto
Miguel Souto is a scholar working on Radiology, Nuclear Medicine and Imaging, Health Informatics and Pulmonary and Respiratory Medicine, having authored 58 papers that have together received 1.0k indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (18 papers), Advanced X-ray and CT Imaging (14 papers) and Digital Radiography and Breast Imaging (11 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (430 citations), Radiology, Nuclear Medicine and Imaging (455 citations) and Health Informatics (26 citations). Miguel Souto has collaborated with scholars based in Spain, United States and France. Frequent co-authors include Pablo G. Tahoces, J Vidal, Marı́a J. Lado, Arturo J. Méndez, Manuel G. Penedo, Celia Varela, José M. Carreira, William A. Pearlman, D. Tucker and G T Barnes. Their work appears in journals such as SHILAP Revista de lepidopterología, Radiology and IEEE Transactions on Medical Imaging.
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.