Farhad Maleki

1.2k total citations
48 papers, 737 citations indexed

About

Farhad Maleki is a scholar working on Molecular Biology, Artificial Intelligence and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Farhad Maleki has authored 48 papers receiving a total of 737 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Molecular Biology, 12 papers in Artificial Intelligence and 12 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Farhad Maleki's work include Radiomics and Machine Learning in Medical Imaging (10 papers), Gene expression and cancer classification (8 papers) and AI in cancer detection (7 papers). Farhad Maleki is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (10 papers), Gene expression and cancer classification (8 papers) and AI in cancer detection (7 papers). Farhad Maleki collaborates with scholars based in Canada, Iran and United States. Farhad Maleki's co-authors include Katie Ovens, Reza Forghani, Caroline Reinhold, Anthony Kusalik, Nikesh Muthukrishnan, Daniel J. Hogan, Behzad Forghani, Ian McQuillan, Rajiv Gupta and Alan Spatz and has published in prestigious journals such as SHILAP Revista de lepidopterología, Bioinformatics and Scientific Reports.

In The Last Decade

Farhad Maleki

40 papers receiving 710 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Farhad Maleki Canada 14 197 170 121 78 63 48 737
Kazuma Kobayashi Japan 14 115 0.6× 241 1.4× 193 1.6× 91 1.2× 42 0.7× 32 638
Tianjun Li China 20 187 0.9× 58 0.3× 141 1.2× 47 0.6× 49 0.8× 57 873
Jingyang Gao China 11 136 0.7× 156 0.9× 163 1.3× 18 0.2× 78 1.2× 55 682
Sebastian Pölsterl Germany 9 126 0.6× 141 0.8× 174 1.4× 17 0.2× 40 0.6× 16 563
Erdal Taşçı Türkiye 11 72 0.4× 182 1.1× 197 1.6× 21 0.3× 40 0.6× 39 532
Guoxing Yang China 16 234 1.2× 76 0.4× 153 1.3× 42 0.5× 46 0.7× 52 755
Surbhi Gupta India 15 81 0.4× 188 1.1× 268 2.2× 42 0.5× 33 0.5× 24 563
Han Yuan Singapore 14 91 0.5× 65 0.4× 206 1.7× 54 0.7× 22 0.3× 55 626
Yu Tian China 22 293 1.5× 164 1.0× 335 2.8× 53 0.7× 78 1.2× 112 1.3k

Countries citing papers authored by Farhad Maleki

Since Specialization
Citations

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

Fields of papers citing papers by Farhad Maleki

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Farhad Maleki

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

All Works

20 of 20 papers shown
1.
2.
Maleki, Farhad, et al.. (2025). Unveiling the potential of bio-based petrochemical development for a sustainable circular economy: A global perspective. Chemical Engineering Journal. 522. 167974–167974.
3.
Maleki, Farhad, et al.. (2025). Exergy and thermoeconomic comparison of sustainable methanol and ammonia production from waste and CO₂. Journal of environmental chemical engineering. 13(5). 119148–119148.
4.
Montaha, Sidratul, et al.. (2025). Federated Pseudo-Labeling: A Data-Centric, Privacy-Preserving Framework for Medical Image Segmentation. IEEE Journal of Biomedical and Health Informatics. 29(12). 8823–8830.
5.
Maleki, Farhad, et al.. (2024). Sustainable hydrogen supply chain development for low-carbon transportation in a fossil-based port region: A case study in a tourism hub. International Journal of Hydrogen Energy. 65. 95–111. 25 indexed citations
6.
Maleki, Farhad, Linda Moy, Reza Forghani, et al.. (2024). RIDGE: Reproducibility, Integrity, Dependability, Generalizability, and Efficiency Assessment of Medical Image Segmentation Models. Journal of Imaging Informatics in Medicine. 38(4). 2524–2536. 2 indexed citations
7.
Bahoric, Boris, et al.. (2024). Deep learning for high-resolution dose prediction in high dose rate brachytherapy for breast cancer treatment. Physics in Medicine and Biology. 69(10). 105011–105011. 2 indexed citations
8.
Rajaram, Ajay, Mina Farag, Farhad Maleki, et al.. (2023). A Deep Learning–Based Approach to Estimate Paneth Cell Granule Area in Celiac Disease. Archives of Pathology & Laboratory Medicine. 148(7). 828–835. 1 indexed citations
9.
Maleki, Farhad, et al.. (2023). OC-0294 Artificial-Intelligence based high precision Brachytherapy dose calculation. Radiotherapy and Oncology. 182. S229–S230. 1 indexed citations
10.
Eramian, Mark, et al.. (2023). Semi-Self-Supervised Learning for Semantic Segmentation in Images with Dense Patterns. Plant Phenomics. 5. 25–25. 11 indexed citations
11.
Maleki, Farhad, Katie Ovens, Rajiv Gupta, et al.. (2022). Generalizability of Machine Learning Models: Quantitative Evaluation of Three Methodological Pitfalls. Radiology Artificial Intelligence. 5(1). e220028–e220028. 67 indexed citations
12.
Azizi, Zahra, Pouria Alipour, Farhad Maleki, et al.. (2022). Importance of sex and gender factors for COVID-19 infection and hospitalisation: a sex-stratified analysis using machine learning in UK Biobank data. BMJ Open. 12(5). e050450–e050450. 8 indexed citations
13.
Saint‐Martin, Christine, et al.. (2022). Radiomics and machine learning for the diagnosis of pediatric cervical non-tuberculous mycobacterial lymphadenitis. Scientific Reports. 12(1). 2962–2962. 11 indexed citations
14.
Mascarella, Marco A., Nikesh Muthukrishnan, Farhad Maleki, et al.. (2021). Above and Beyond Age: Prediction of Major Postoperative Adverse Events in Head and Neck Surgery. Annals of Otology Rhinology & Laryngology. 131(7). 697–703. 13 indexed citations
15.
Liu, Xiaoyang, Farhad Maleki, Nikesh Muthukrishnan, et al.. (2021). Site-Specific Variation in Radiomic Features of Head and Neck Squamous Cell Carcinoma and Its Impact on Machine Learning Models. Cancers. 13(15). 3723–3723. 5 indexed citations
16.
Agarwal, Mohit, et al.. (2020). Dual Energy Computed Tomography in Head and Neck Imaging. Neuroimaging Clinics of North America. 30(3). 311–323. 20 indexed citations
17.
Muthukrishnan, Nikesh, Farhad Maleki, Katie Ovens, et al.. (2020). Brief History of Artificial Intelligence. Neuroimaging Clinics of North America. 30(4). 393–399. 100 indexed citations
18.
Maleki, Farhad, et al.. (2020). Overview of Machine Learning Part 1. Neuroimaging Clinics of North America. 30(4). e17–e32. 24 indexed citations
19.
Maleki, Farhad, et al.. (2019). Method Choice in Gene Set Analysis Has Important Consequences for Analysis Outcome.. Bioinformatics. 43–54. 1 indexed citations
20.
Maleki, Farhad, Katie Ovens, Ian McQuillan, & Anthony Kusalik. (2019). Size matters: how sample size affects the reproducibility and specificity of gene set analysis. Human Genomics. 13(S1). 42–42. 35 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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