Maria Frucci
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence top 10%
- Radiology, Nuclear Medicine and Imaging top 10%
- Signal Processing top 10%
- Human-Computer Interaction top 5%
- Co-authors
- Daniel RiccioNadia BrancatiGabriella Sanniti di BajaGiuseppe De PietroLuigi GalloDiego GragnanielloMichele NappiGiuseppe Caggianese
- Topics
- Medical Image Segmentation Techniques (10 papers)AI in cancer detection (7 papers)Retinal Imaging and Analysis (7 papers)
- Partner nations
- ItalyUnited StatesSwitzerland
In The Last Decade
Maria Frucci
39 papers receiving 542 citations
Peers
Comparison fields: 5 of 104
- Computer Vision and Pattern Recognition 272
- Artificial Intelligence 193
- Radiology, Nuclear Medicine and Imaging 159
- Signal Processing 68
- Human-Computer Interaction 57
Countries citing papers authored by Maria Frucci
This map shows the geographic impact of Maria Frucci'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 Maria Frucci with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maria Frucci more than expected).
Fields of papers citing papers by Maria Frucci
This network shows the impact of papers produced by Maria Frucci. 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 Maria Frucci. The network helps show where Maria Frucci may publish in the future.
Co-authorship network of co-authors of Maria Frucci
This figure shows the co-authorship network connecting the top 25 collaborators of Maria Frucci. A scholar is included among the top collaborators of Maria Frucci 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 Maria Frucci. Maria Frucci 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 | 11 | |
| 3 | 65 | |
| 4 | 20 | |
| 5 | Hierarchical Cell-to-Tissue Graph Representations for Breast Cancer Subtyping in Digital Pathology. | 3 |
| 6 | 24 | |
| 7 | 9 | |
| 8 | 29 | |
| 9 | 6 | |
| 10 | 27 | |
| 11 | 27 | |
| 12 | 16 | |
| 13 | 30 | |
| 14 | 12 | |
| 15 | 9 | |
| 16 | 3 | |
| 17 | 21 | |
| 18 | 4 | |
| 19 | A Novel Merging Method in Watershed Segmentation. | 1 |
| 20 | 1 |
About Maria Frucci
Maria Frucci is a scholar working on Computer Vision and Pattern Recognition, Biophysics and Ophthalmology, having authored 40 papers that have together received 574 indexed citations. Recurring topics across this work include Medical Image Segmentation Techniques (10 papers), AI in cancer detection (7 papers) and Retinal Imaging and Analysis (7 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (272 citations), Human-Computer Interaction (57 citations) and Biophysics (52 citations). Maria Frucci has collaborated with scholars based in Italy, United States and Switzerland. Frequent co-authors include Daniel Riccio, Nadia Brancati, Gabriella Sanniti di Baja, Giuseppe De Pietro, Luigi Gallo, Diego Gragnaniello, Michele Nappi, Giuseppe Caggianese, Chiara Galdi and Andrea F. Abate. Their work appears in journals such as Expert Systems with Applications, IEEE Access and Pattern Recognition.
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.