Nadia Brancati
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition top 5%
- Radiology, Nuclear Medicine and Imaging top 10%
- Biophysics top 10%
- Human-Computer Interaction top 10%
- Co-authors
- Maria FrucciGiuseppe De PietroDaniel RiccioLuigi GalloDiego GragnanielloGiuseppe CaggianeseGiovanna SanninoLaura Verde
- Topics
- AI in cancer detection (9 papers)Radiomics and Machine Learning in Medical Imaging (7 papers)Cell Image Analysis Techniques (4 papers)
- Journals
- IEEE AccessSensorsNeurocomputing
- Partner nations
- ItalySwitzerland
In The Last Decade
Nadia Brancati
22 papers receiving 352 citations
Peers
Comparison fields: 5 of 90
- Artificial Intelligence 173
- Computer Vision and Pattern Recognition 148
- Radiology, Nuclear Medicine and Imaging 118
- Biophysics 47
- Human-Computer Interaction 42
Countries citing papers authored by Nadia Brancati
This map shows the geographic impact of Nadia Brancati'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 Nadia Brancati with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nadia Brancati more than expected).
Fields of papers citing papers by Nadia Brancati
This network shows the impact of papers produced by Nadia Brancati. 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 Nadia Brancati. The network helps show where Nadia Brancati may publish in the future.
Co-authorship network of co-authors of Nadia Brancati
This figure shows the co-authorship network connecting the top 25 collaborators of Nadia Brancati. A scholar is included among the top collaborators of Nadia Brancati 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 Nadia Brancati. Nadia Brancati 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 | 1 | |
| 3 | 3 | |
| 4 | 11 | |
| 5 | 65 | |
| 6 | 1 | |
| 7 | 20 | |
| 8 | Hierarchical Cell-to-Tissue Graph Representations for Breast Cancer Subtyping in Digital Pathology. | 3 |
| 9 | 16 | |
| 10 | 24 | |
| 11 | 9 | |
| 12 | 58 | |
| 13 | 29 | |
| 14 | 6 | |
| 15 | 16 | |
| 16 | 27 | |
| 17 | 16 | |
| 18 | 1 | |
| 19 | 12 | |
| 20 | 9 |
About Nadia Brancati
Nadia Brancati is a scholar working on Biophysics, Human-Computer Interaction and Radiology, Nuclear Medicine and Imaging, having authored 23 papers that have together received 363 indexed citations. Recurring topics across this work include AI in cancer detection (9 papers), Radiomics and Machine Learning in Medical Imaging (7 papers) and Cell Image Analysis Techniques (4 papers). The work is most often cited by research in Biophysics (47 citations), Human-Computer Interaction (42 citations) and Computer Vision and Pattern Recognition (148 citations). Nadia Brancati has collaborated with scholars based in Italy and Switzerland. Frequent co-authors include Maria Frucci, Giuseppe De Pietro, Daniel Riccio, Luigi Gallo, Diego Gragnaniello, Giuseppe Caggianese, Giovanna Sannino, Laura Verde, Maurizio Di Bonito and Florinda Feroce. Their work appears in journals such as IEEE Access, Sensors and Neurocomputing.
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.