Sabina Tangaro
- Radiology, Nuclear Medicine and Imaging top 2%
- Artificial Intelligence top 2%
- Cognitive Neuroscience top 5%
- Pulmonary and Respiratory Medicine top 10%
- Molecular Biology
- Co-authors
- R. BellottiNicola AmorosoA. MonacoAngela LombardiMarianna La RoccaDomenico DiaconoAnnarita FanizziLoredana Bellantuono
- Topics
- Functional Brain Connectivity Studies (26 papers)AI in cancer detection (25 papers)Radiomics and Machine Learning in Medical Imaging (22 papers)
- Journals
- Proceedings of the National Academy of SciencesSHILAP Revista de lepidopterologíaPLoS ONE
- Partner nations
- ItalyUnited StatesUnited Kingdom
In The Last Decade
Sabina Tangaro
134 papers receiving 2.4k citations
Peers
Comparison fields: 5 of 169
- Radiology, Nuclear Medicine and Imaging 855
- Artificial Intelligence 723
- Cognitive Neuroscience 365
- Pulmonary and Respiratory Medicine 326
- Molecular Biology 309
Countries citing papers authored by Sabina Tangaro
This map shows the geographic impact of Sabina Tangaro'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 Sabina Tangaro with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sabina Tangaro more than expected).
Fields of papers citing papers by Sabina Tangaro
This network shows the impact of papers produced by Sabina Tangaro. 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 Sabina Tangaro. The network helps show where Sabina Tangaro may publish in the future.
Co-authorship network of co-authors of Sabina Tangaro
This figure shows the co-authorship network connecting the top 25 collaborators of Sabina Tangaro. A scholar is included among the top collaborators of Sabina Tangaro 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 Sabina Tangaro. Sabina Tangaro 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 | 0 | |
| 4 | 3 | |
| 5 | 1 | |
| 6 | 3 | |
| 7 | 1 | |
| 8 | 39 | |
| 9 | 5 | |
| 10 | 20 | |
| 11 | 0 | |
| 12 | 16 | |
| 13 | 49 | |
| 14 | 20 | |
| 15 | 13 | |
| 16 | 24 | |
| 17 | Active Learning Machines for Automatic Segmentation of Hippocampus in MRI. | 4 |
| 18 | Mass lesion detection in mammographic images using Haralik textural features | 1 |
| 19 | DISSIMILARITY APPLICATION FOR MEDICAL IMAGING CLASSIFICATION | 2 |
| 20 | 14 |
About Sabina Tangaro
Sabina Tangaro is a scholar working on Health Informatics, Radiology, Nuclear Medicine and Imaging and Cognitive Neuroscience, having authored 145 papers that have together received 2.5k indexed citations. Recurring topics across this work include Functional Brain Connectivity Studies (26 papers), AI in cancer detection (25 papers) and Radiomics and Machine Learning in Medical Imaging (22 papers). The work is most often cited by research in Health Informatics (61 citations), Radiology, Nuclear Medicine and Imaging (855 citations) and Neurology (284 citations). Sabina Tangaro has collaborated with scholars based in Italy, United States and United Kingdom. Frequent co-authors include R. Bellotti, Nicola Amoroso, A. Monaco, Angela Lombardi, Marianna La Rocca, Domenico Diacono, Annarita Fanizzi, Loredana Bellantuono, T. Maggipinto and Raffaella Massafra. Their work appears in journals such as Proceedings of the National Academy of Sciences, SHILAP Revista de lepidopterología and PLoS ONE.
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.