Maria Gabrani

970 total citations
39 papers, 469 citations indexed

About

Maria Gabrani is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Maria Gabrani has authored 39 papers receiving a total of 469 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Artificial Intelligence, 18 papers in Computer Vision and Pattern Recognition and 11 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Maria Gabrani's work include AI in cancer detection (14 papers), Radiomics and Machine Learning in Medical Imaging (10 papers) and Advancements in Photolithography Techniques (9 papers). Maria Gabrani is often cited by papers focused on AI in cancer detection (14 papers), Radiomics and Machine Learning in Medical Imaging (10 papers) and Advancements in Photolithography Techniques (9 papers). Maria Gabrani collaborates with scholars based in Switzerland, United States and United Kingdom. Maria Gabrani's co-authors include Pushpak Pati, Antonio Foncubierta–Rodríguez, Orçun Göksel, Matheus P. Viana, Dávid Lányi, Aditya Kashyap, Peter J. Wild, Anna Fomitcheva Khartchenko, Govind V. Kaigala and Haralampos Pozidis and has published in prestigious journals such as IEEE Transactions on Image Processing, The Journal of Urology and Trends in biotechnology.

In The Last Decade

Maria Gabrani

37 papers receiving 452 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Maria Gabrani Switzerland	12	254	195	164	58	55	39	469
Najah Alsubaie Saudi Arabia	14	330 1.3×	242 1.2×	161 1.0×	87 1.5×	38 0.7×	35	654
Heung‐Kook Choi South Korea	16	259 1.0×	210 1.1×	334 2.0×	80 1.4×	57 1.0×	89	814
Ayelet Akselrod-Ballin Israel	11	181 0.7×	228 1.2×	162 1.0×	52 0.9×	32 0.6×	20	516
Monjoy Saha India	10	371 1.5×	269 1.4×	172 1.0×	39 0.7×	91 1.7×	18	559
Davis Foote United States	2	314 1.2×	169 0.9×	62 0.4×	40 0.7×	24 0.4×	2	419
Spiros Kostopoulos Greece	14	249 1.0×	306 1.6×	128 0.8×	115 2.0×	52 0.9×	67	617
Xiaofei Luo Japan	4	241 0.9×	165 0.8×	136 0.8×	44 0.8×	38 0.7×	6	370
Mehrdad J. Gangeh Canada	17	299 1.2×	414 2.1×	173 1.1×	159 2.7×	45 0.8×	34	719
David Tellez Netherlands	7	480 1.9×	310 1.6×	243 1.5×	35 0.6×	84 1.5×	9	592

Countries citing papers authored by Maria Gabrani

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

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20 of 20 papers shown

Heller, Nicholas, Betty Wang, Rebecca A. Campbell, et al.. (2025). AUTOMATING RENAL CANCER CHART REVIEW USING LARGE LANGUAGE MODELS. Urologic Oncology Seminars and Original Investigations. 43(3). 57–58. 1 indexed citations

Portenier, Tiziano, et al.. (2024). Generative feature-driven image replay for continual learning. Image and Vision Computing. 150. 105187–105187. 5 indexed citations

Şahin, Mehmet, Benjamin C. B. Symons, Pushpak Pati, et al.. (2024). Efficient Parameter Optimisation for Quantum Kernel Alignment: A Sub-sampling Approach in Variational Training. Quantum. 8. 1502–1502. 4 indexed citations

Pati, Pushpak, et al.. (2023). Weakly supervised joint whole-slide segmentation and classification in prostate cancer. Medical Image Analysis. 89. 102915–102915. 17 indexed citations

Brancati, Nadia, Anna Maria Anniciello, Pushpak Pati, et al.. (2022). BRACS: A Dataset for BReAst Carcinoma Subtyping in H&E Histology Images. Database. 2022. 65 indexed citations

Born, Jannis, David Beymer, Deepta Rajan, et al.. (2021). On the role of artificial intelligence in medical imaging of COVID-19. Patterns. 2(6). 100269–100269. 38 indexed citations

Pati, Pushpak, Guillaume Jaume, Florinda Feroce, et al.. (2021). Hierarchical Cell-to-Tissue Graph Representations for Breast Cancer Subtyping in Digital Pathology.. arXiv (Cornell University). 3 indexed citations

Kashyap, Aditya, Maria Anna Rapsomaniki, Anna Fomitcheva Khartchenko, et al.. (2021). Quantification of tumor heterogeneity: from data acquisition to metric generation. Trends in biotechnology. 40(6). 647–676. 46 indexed citations

Born, Jannis, David Beymer, Deepta Rajan, et al.. (2021). On the role of artificial intelligence in medical imaging of COVID-19. Patterns. 2(8). 100330–100330. 11 indexed citations

10.

Tsai, Hung‐Wen, et al.. (2021). Using Stain Decomposition for Nucleus Segmentation on Multisource H&E Slide Images. 2021 IEEE Symposium Series on Computational Intelligence (SSCI). 1–7. 3 indexed citations

11.

Kashyap, Aditya, Anna Fomitcheva Khartchenko, Pushpak Pati, et al.. (2019). Quantitative microimmunohistochemistry for the grading of immunostains on tumour tissues. Nature Biomedical Engineering. 3(6). 478–490. 24 indexed citations

12.

Pati, Pushpak, Raúl Catena, Orçun Göksel, & Maria Gabrani. (2019). A deep learning framework for context-aware mitotic activity estimation in whole slide images. 4. 7–7. 2 indexed citations

13.

Pati, Pushpak, Aditya Kashyap, Anna Fomitcheva Khartchenko, et al.. (2019). High-Quality Immunohistochemical Stains Through Computational Assay Parameter Optimization. IEEE Transactions on Biomedical Engineering. 66(10). 2952–2963. 8 indexed citations

14.

Anghel, Andreea, Miloš Stanisavljević, Nikolaos Papandreou, et al.. (2019). A High-Performance System for Robust Stain Normalization of Whole-Slide Images in Histopathology. Frontiers in Medicine. 6. 193–193. 68 indexed citations

15.

Binder, Thomas, et al.. (2019). Multi-Organ Gland Segmentation Using Deep Learning. Frontiers in Medicine. 6. 173–173. 30 indexed citations

16.

Zhong, Qing, et al.. (2016). A computational framework for disease grading using protein signatures. 1401–1404. 4 indexed citations

17.

Estellers, Virginia, Jean‐Philippe Thiran, & Maria Gabrani. (2014). Surface Reconstruction From Microscopic Images in Optical Lithography. IEEE Transactions on Image Processing. 23(8). 3560–3573. 6 indexed citations

18.

Gabrani, Maria, Andrés Torres, Sujoy Sarkar, et al.. (2011). Design specific joint optimization of masks and sources on a very large scale. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 7973. 797308–797308. 12 indexed citations

19.

Stanisavljević, Miloš, et al.. (2007). Case study of fault-tolerant architectures for 90nm CMOS crythographic cores. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 253–256. 1 indexed citations

20.

Döring, Andreas & Maria Gabrani. (2006). On networking multithreaded processor design: Hardware thread prioritization. 1. 520–523. 3 indexed citations

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.

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