Fadila Zerka

1.1k total citations · 2 hit papers
14 papers, 646 citations indexed

About

Fadila Zerka is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Artificial Intelligence. According to data from OpenAlex, Fadila Zerka has authored 14 papers receiving a total of 646 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Radiology, Nuclear Medicine and Imaging, 7 papers in Pulmonary and Respiratory Medicine and 6 papers in Artificial Intelligence. Recurrent topics in Fadila Zerka's work include Radiomics and Machine Learning in Medical Imaging (11 papers), Privacy-Preserving Technologies in Data (4 papers) and Advanced X-ray and CT Imaging (4 papers). Fadila Zerka is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (11 papers), Privacy-Preserving Technologies in Data (4 papers) and Advanced X-ray and CT Imaging (4 papers). Fadila Zerka collaborates with scholars based in Netherlands, Belgium and Norway. Fadila Zerka's co-authors include Philippe Lambin, Ralph T. H. Leijenaar, Seán Walsh, Benjamin Miraglio, Akshayaa Vaidyanathan, Henry C. Woodruff, Wim Vos, Sergey Primakov, Abdalla Ibrahim and Turkey Refaee and has published in prestigious journals such as Journal of Clinical Oncology, Scientific Reports and International Journal of Molecular Sciences.

In The Last Decade

Fadila Zerka

14 papers receiving 639 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Radiomics: from qualitative to quantitative imaging

2020 224 citations William Rogers, Turkey Refaee et al. British Journal of Radiology profile →
A review in radiomics: Making personalized medicine a reality via routine imaging

2021 192 citations Julien Guiot, Akshayaa Vaidyanathan et al. Medicinal Research Reviews profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Fadila Zerka Netherlands	7	418	155	151	124	73	14	646
David Clunie United States	19	351 0.8×	209 1.3×	244 1.6×	104 0.8×	72 1.0×	44	838
Akshayaa Vaidyanathan Netherlands	10	268 0.6×	111 0.7×	79 0.5×	84 0.7×	43 0.6×	18	472
Lise Wei United States	13	627 1.5×	218 1.4×	176 1.2×	197 1.6×	117 1.6×	24	860
Aydın Demircioğlu Germany	16	545 1.3×	186 1.2×	132 0.9×	168 1.4×	101 1.4×	53	861
Benjamin Miraglio Netherlands	4	203 0.5×	81 0.5×	104 0.7×	60 0.5×	34 0.5×	7	380
Paul Desbordes France	9	285 0.7×	104 0.7×	142 0.9×	71 0.6×	43 0.6×	13	534
Cheng Jin China	13	577 1.4×	160 1.0×	212 1.4×	123 1.0×	169 2.3×	33	901
Marco Alì Italy	15	260 0.6×	91 0.6×	55 0.4×	108 0.9×	77 1.1×	54	623
Behzad Forghani Canada	8	488 1.2×	119 0.8×	100 0.7×	181 1.5×	62 0.8×	9	649
Qing Fu China	9	553 1.3×	139 0.9×	267 1.8×	59 0.5×	45 0.6×	32	710

Countries citing papers authored by Fadila Zerka

Since Specialization

Citations

This map shows the geographic impact of Fadila Zerka'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 Fadila Zerka with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fadila Zerka more than expected).

Fields of papers citing papers by Fadila Zerka

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Fadila Zerka. 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 Fadila Zerka. The network helps show where Fadila Zerka may publish in the future.

Co-authorship network of co-authors of Fadila Zerka

This figure shows the co-authorship network connecting the top 25 collaborators of Fadila Zerka. A scholar is included among the top collaborators of Fadila Zerka 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 Fadila Zerka. Fadila Zerka is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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14 of 14 papers shown

Zerka, Fadila, et al.. (2025). Optimizing Federated Learning Configurations for MRI Prostate Segmentation and Cancer Detection: A Simulation Study. Radiology Artificial Intelligence. 7(5). e240485–e240485. 1 indexed citations

Zerka, Fadila, et al.. (2024). Federated learning for prostate cancer detection in biparametric MRI: optimization of rounds, epochs, and aggregation strategy. University of Groningen research database (University of Groningen / Centre for Information Technology). 76–76. 1 indexed citations

Bodard, Sylvain, et al.. (2023). Multicenter evaluation of AI-based CT radiomics for EGFR mutation prediction in NSCLC.. Journal of Clinical Oncology. 41(16_suppl). e20515–e20515. 1 indexed citations

Zerka, Fadila, et al.. (2023). Systematic Review, Meta-Analysis and Radiomics Quality Score Assessment of CT Radiomics-Based Models Predicting Tumor EGFR Mutation Status in Patients with Non-Small-Cell Lung Cancer. International Journal of Molecular Sciences. 24(14). 11433–11433. 18 indexed citations

Vaidyanathan, Akshayaa, Julien Guiot, Fadila Zerka, et al.. (2022). An externally validated fully automated deep learning algorithm to classify COVID-19 and other pneumonias on chest computed tomography. ERJ Open Research. 8(2). 579–2021. 6 indexed citations

Vaidyanathan, Akshayaa, Ralph T. H. Leijenaar, Marc van Hoof, et al.. (2021). Deep learning for the fully automated segmentation of the inner ear on MRI. Scientific Reports. 11(1). 2885–2885. 44 indexed citations

Zerka, Fadila, Visara Urovi, Fabio Bottari, et al.. (2021). Privacy preserving distributed learning classifiers – Sequential learning with small sets of data. Computers in Biology and Medicine. 136. 104716–104716. 15 indexed citations

Guiot, Julien, Akshayaa Vaidyanathan, Louis Deprez, et al.. (2021). A review in radiomics: Making personalized medicine a reality via routine imaging. Medicinal Research Reviews. 42(1). 426–440. 192 indexed citations breakdown →

Leijenaar, Ralph T. H., Fadila Zerka, Akshayaa Vaidyanathan, et al.. (2020). 5MO Prospective validation of a radiomics signature for chemoradiotherapy lung cancer patients. Annals of Oncology. 31. S246–S246. 1 indexed citations

10.

Zerka, Fadila, Akshayaa Vaidyanathan, Julien Guiot, et al.. (2020). Late Breaking Abstract - Development and validation of an automated radiomic CT signature for detecting?COVID-19. 4152–4152. 1 indexed citations

11.

Leijenaar, Ralph T. H., et al.. (2020). OC-0587: Prospective Validation of a Radiomics Signature for Chemoradiotherapy Lung Cancer Patients. Radiotherapy and Oncology. 152. S330–S331. 1 indexed citations

12.

Zerka, Fadila, Seán Walsh, Marta Bogowicz, et al.. (2020). Systematic Review of Privacy-Preserving Distributed Machine Learning From Federated Databases in Health Care. JCO Clinical Cancer Informatics. 4(4). 184–200. 80 indexed citations

13.

Zerka, Fadila, Visara Urovi, Akshayaa Vaidyanathan, et al.. (2020). Blockchain for Privacy Preserving and Trustworthy Distributed Machine Learning in Multicentric Medical Imaging (C-DistriM). IEEE Access. 8. 183939–183951. 61 indexed citations

14.

Rogers, William, Turkey Refaee, R. Lieverse, et al.. (2020). Radiomics: from qualitative to quantitative imaging. British Journal of Radiology. 93(1108). 20190948–20190948. 224 indexed citations breakdown →

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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