Cecilia Di Ruberto
- Computer Vision and Pattern Recognition top 1%
- Artificial Intelligence top 5%
- Plant Science top 10%
- Biophysics top 1%
- Radiology, Nuclear Medicine and Imaging top 5%
- Co-authors
- Lorenzo PutzuAndrea LoddoAndrew G. DempsterGiovanni CaocciShahid M. KhanM. LoddoMichel KocherG. Rodríguez
- Topics
- Digital Imaging for Blood Diseases (21 papers)Image Retrieval and Classification Techniques (15 papers)AI in cancer detection (14 papers)
- Partner nations
- ItalyUnited KingdomSwitzerland
In The Last Decade
Cecilia Di Ruberto
58 papers receiving 1.3k citations
Peers
Comparison fields: 5 of 108
- Computer Vision and Pattern Recognition 945
- Artificial Intelligence 415
- Plant Science 297
- Biophysics 263
- Radiology, Nuclear Medicine and Imaging 255
Countries citing papers authored by Cecilia Di Ruberto
This map shows the geographic impact of Cecilia Di Ruberto'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 Cecilia Di Ruberto with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Cecilia Di Ruberto more than expected).
Fields of papers citing papers by Cecilia Di Ruberto
This network shows the impact of papers produced by Cecilia Di Ruberto. 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 Cecilia Di Ruberto. The network helps show where Cecilia Di Ruberto may publish in the future.
Co-authorship network of co-authors of Cecilia Di Ruberto
This figure shows the co-authorship network connecting the top 25 collaborators of Cecilia Di Ruberto. A scholar is included among the top collaborators of Cecilia Di Ruberto 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 Cecilia Di Ruberto. Cecilia Di Ruberto 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 | 2 | |
| 3 | 0 | |
| 4 | 1 | |
| 5 | 8 | |
| 6 | 11 | |
| 7 | 1 | |
| 8 | 9 | |
| 9 | 3 | |
| 10 | 17 | |
| 11 | 101 | |
| 12 | 57 | |
| 13 | 12 | |
| 14 | 201 | |
| 15 | White Blood Cells Identication and Classication from Leukemic Blood Image. | 1 |
| 16 | Dominant points detection on digital curves: A comparison between optimal and exact approaches | 1 |
| 17 | A new iterative approach for dominant points extraction in planar curves | 3 |
| 18 | A fast iterative method for dominant points detection of digital curves | 1 |
| 19 | 1 | |
| 20 | 4 |
About Cecilia Di Ruberto
Cecilia Di Ruberto is a scholar working on Computer Vision and Pattern Recognition, Biophysics and Health Informatics, having authored 65 papers that have together received 1.3k indexed citations. Recurring topics across this work include Digital Imaging for Blood Diseases (21 papers), Image Retrieval and Classification Techniques (15 papers) and AI in cancer detection (14 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (945 citations), Biophysics (263 citations) and Media Technology (249 citations). Cecilia Di Ruberto has collaborated with scholars based in Italy, United Kingdom and Switzerland. Frequent co-authors include Lorenzo Putzu, Andrea Loddo, Andrew G. Dempster, Giovanni Caocci, Shahid M. Khan, M. Loddo, Michel Kocher, G. Rodríguez, Giovanni Puglisi and Michele Nappi. Their work appears in journals such as IEEE Access, Sensors 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.