Nikesh Muthukrishnan

706 total citations
10 papers, 454 citations indexed

About

Nikesh Muthukrishnan is a scholar working on Radiology, Nuclear Medicine and Imaging, Surgery and Biomedical Engineering. According to data from OpenAlex, Nikesh Muthukrishnan has authored 10 papers receiving a total of 454 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Radiology, Nuclear Medicine and Imaging, 3 papers in Surgery and 3 papers in Biomedical Engineering. Recurrent topics in Nikesh Muthukrishnan's work include Radiomics and Machine Learning in Medical Imaging (7 papers), Advanced X-ray and CT Imaging (3 papers) and Medical Imaging Techniques and Applications (2 papers). Nikesh Muthukrishnan is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (7 papers), Advanced X-ray and CT Imaging (3 papers) and Medical Imaging Techniques and Applications (2 papers). Nikesh Muthukrishnan collaborates with scholars based in Canada, United States and Germany. Nikesh Muthukrishnan's co-authors include Reza Forghani, Caroline Reinhold, Behzad Forghani, Farhad Maleki, Katie Ovens, Avishek Chatterjee, Peter Savadjiev, Julian L. Wichmann, Griselda Romero-Sánchez and Eugene Yu and has published in prestigious journals such as Blood, Scientific Reports and Cancers.

In The Last Decade

Nikesh Muthukrishnan

10 papers receiving 434 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Nikesh Muthukrishnan Canada 8 259 96 92 75 75 10 454
Andrew Colucci United States 5 227 0.9× 84 0.9× 71 0.8× 75 1.0× 74 1.0× 8 380
Behzad Forghani Canada 8 488 1.9× 100 1.0× 181 2.0× 119 1.6× 76 1.0× 9 649
Avi Ben-Cohen Israel 8 262 1.0× 209 2.2× 118 1.3× 68 0.9× 79 1.1× 10 594
Benjamin Miraglio Netherlands 4 203 0.8× 104 1.1× 60 0.7× 81 1.1× 56 0.7× 7 380
Maurizio Cè Italy 12 276 1.1× 100 1.0× 127 1.4× 132 1.8× 103 1.4× 42 531
Özer Çelik Türkiye 19 228 0.9× 207 2.2× 256 2.8× 53 0.7× 117 1.6× 92 1.1k
Christoph Haarburger Germany 11 446 1.7× 310 3.2× 93 1.0× 77 1.0× 80 1.1× 16 676
Ge-Ge Wu China 9 479 1.8× 318 3.3× 100 1.1× 93 1.2× 103 1.4× 10 710
Liesbeth Vandewinckele Belgium 7 398 1.5× 139 1.4× 129 1.4× 112 1.5× 114 1.5× 11 593

Countries citing papers authored by Nikesh Muthukrishnan

Since Specialization
Citations

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

Fields of papers citing papers by Nikesh Muthukrishnan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Nikesh Muthukrishnan

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

All Works

10 of 10 papers shown
1.
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
2.
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
3.
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
4.
Santiago, Raoul, Reza Forghani, Nikesh Muthukrishnan, et al.. (2021). CT-based radiomics model with machine learning for predicting primary treatment failure in diffuse large B-cell Lymphoma. Translational Oncology. 14(10). 101188–101188. 11 indexed citations
5.
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
6.
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
7.
Maleki, Farhad, Nikesh Muthukrishnan, Katie Ovens, Caroline Reinhold, & Reza Forghani. (2020). Machine Learning Algorithm Validation. Neuroimaging Clinics of North America. 30(4). 433–445. 89 indexed citations
8.
Forghani, Reza, Peter Savadjiev, Avishek Chatterjee, et al.. (2019). Radiomics and Artificial Intelligence for Biomarker and Prediction Model Development in Oncology. Computational and Structural Biotechnology Journal. 17. 995–1008. 133 indexed citations
9.
Forghani, Behzad, Caroline Reinhold, Griselda Romero-Sánchez, et al.. (2019). Dual-Energy CT Texture Analysis With Machine Learning for the Evaluation and Characterization of Cervical Lymphadenopathy. Computational and Structural Biotechnology Journal. 17. 1009–1015. 70 indexed citations
10.
Santiago, Raoul, Reza Forghani, Nikesh Muthukrishnan, et al.. (2019). Prediction of High-Risk Group of Primary Refractory Diffuse Large B-Cell Lymphoma (DLBCL) Patients Using a CT-Based Radiomics Model with Machine Learning. Blood. 134(Supplement_1). 4136–4136. 2 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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2026