Maria Gabrani
- Artificial Intelligence top 5%
- Radiology, Nuclear Medicine and Imaging top 10%
- Computer Vision and Pattern Recognition top 5%
- Biomedical Engineering
- Biophysics top 5%
- Co-authors
- Pushpak PatiAntonio Foncubierta–RodríguezOrçun GökselMatheus P. VianaDávid LányiPeter J. WildAditya KashyapGovind V. Kaigala
- Topics
- AI in cancer detection (14 papers)Radiomics and Machine Learning in Medical Imaging (10 papers)Advancements in Photolithography Techniques (9 papers)
- Partner nations
- SwitzerlandUnited StatesUnited Kingdom
In The Last Decade
Maria Gabrani
37 papers receiving 452 citations
Peers
Comparison fields: 5 of 81
- Artificial Intelligence 254
- Radiology, Nuclear Medicine and Imaging 195
- Computer Vision and Pattern Recognition 164
- Biomedical Engineering 58
- Biophysics 55
Countries citing papers authored by Maria Gabrani
This map shows the geographic impact of Maria Gabrani'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 Gabrani with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maria Gabrani more than expected).
Fields of papers citing papers by Maria Gabrani
This network shows the impact of papers produced by Maria Gabrani. 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 Gabrani. The network helps show where Maria Gabrani may publish in the future.
Co-authorship network of co-authors of Maria Gabrani
This figure shows the co-authorship network connecting the top 25 collaborators of Maria Gabrani. A scholar is included among the top collaborators of Maria Gabrani 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 Gabrani. Maria Gabrani 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 | 5 | |
| 3 | 4 | |
| 4 | 17 | |
| 5 | 65 | |
| 6 | Hierarchical Cell-to-Tissue Graph Representations for Breast Cancer Subtyping in Digital Pathology. | 3 |
| 7 | 38 | |
| 8 | 46 | |
| 9 | 3 | |
| 10 | 11 | |
| 11 | 25 | |
| 12 | 24 | |
| 13 | 68 | |
| 14 | 30 | |
| 15 | 8 | |
| 16 | 2 | |
| 17 | 4 | |
| 18 | 6 | |
| 19 | 12 | |
| 20 | 1 |
About Maria Gabrani
Maria Gabrani is a scholar working on Biophysics, Computer Graphics and Computer-Aided Design and Computer Vision and Pattern Recognition, having authored 39 papers that have together received 469 indexed citations. Recurring topics across this work include AI in cancer detection (14 papers), Radiomics and Machine Learning in Medical Imaging (10 papers) and Advancements in Photolithography Techniques (9 papers). The work is most often cited by research in Health Informatics (31 citations), Biophysics (55 citations) and Radiology, Nuclear Medicine and Imaging (195 citations). Maria Gabrani has collaborated with scholars based in Switzerland, United States and United Kingdom. Frequent co-authors include Pushpak Pati, Antonio Foncubierta–Rodríguez, Orçun Göksel, Matheus P. Viana, Dávid Lányi, Peter J. Wild, Aditya Kashyap, Govind V. Kaigala, Anna Fomitcheva Khartchenko and Haralampos Pozidis. Their work appears in journals such as IEEE Transactions on Image Processing, The Journal of Urology and Trends in biotechnology.
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.