Khashayar Namdar

780 total citations
26 papers, 470 citations indexed

About

Khashayar Namdar is a scholar working on Radiology, Nuclear Medicine and Imaging, Genetics and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Khashayar Namdar has authored 26 papers receiving a total of 470 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Radiology, Nuclear Medicine and Imaging, 8 papers in Genetics and 6 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Khashayar Namdar's work include Radiomics and Machine Learning in Medical Imaging (15 papers), Glioma Diagnosis and Treatment (8 papers) and MRI in cancer diagnosis (3 papers). Khashayar Namdar is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (15 papers), Glioma Diagnosis and Treatment (8 papers) and MRI in cancer diagnosis (3 papers). Khashayar Namdar collaborates with scholars based in Canada, United States and Germany. Khashayar Namdar's co-authors include Farzad Khalvati, Matthias Wagner, Masoom A. Haider, Lin Liu, Birgit Ertl‐Wagner, Asthik Biswas, Kartik Jhaveri, Sandra E. Fischer, Xiaoyang Liu and Sara Lewis and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and Transplantation.

In The Last Decade

Khashayar Namdar

24 papers receiving 464 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Khashayar Namdar Canada 10 280 109 86 81 71 26 470
Khaled Bousabarah United States 11 312 1.1× 143 1.3× 93 1.1× 48 0.6× 73 1.0× 29 492
Martijn P. A. Starmans Netherlands 12 287 1.0× 168 1.5× 56 0.7× 59 0.7× 63 0.9× 29 401
Paul Blanc‐Durand France 13 331 1.2× 117 1.1× 35 0.4× 37 0.5× 85 1.2× 31 554
Anselm Schulz Norway 9 266 0.9× 53 0.5× 110 1.3× 19 0.2× 79 1.1× 36 417
Ramón Correa United States 11 372 1.3× 110 1.0× 198 2.3× 93 1.1× 85 1.2× 30 526
Katerina Nikiforaki Greece 10 225 0.8× 51 0.5× 23 0.3× 89 1.1× 40 0.6× 39 344
Valentina Brancato Italy 16 225 0.8× 135 1.2× 31 0.4× 50 0.6× 44 0.6× 39 566
Christoph Fürweger Germany 12 194 0.7× 128 1.2× 41 0.5× 42 0.5× 69 1.0× 59 467
Mehrdad Oveisi Iran 14 691 2.5× 241 2.2× 69 0.8× 140 1.7× 281 4.0× 41 873
Vishwa S. Parekh United States 14 722 2.6× 154 1.4× 49 0.6× 252 3.1× 213 3.0× 39 942

Countries citing papers authored by Khashayar Namdar

Since Specialization
Citations

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

Fields of papers citing papers by Khashayar Namdar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Khashayar Namdar

This figure shows the co-authorship network connecting the top 25 collaborators of Khashayar Namdar. A scholar is included among the top collaborators of Khashayar Namdar 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 Khashayar Namdar. Khashayar Namdar 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.
Namdar, Khashayar, S. Carey, Sandra E. Fischer, et al.. (2025). Non-invasive liver fibrosis screening on CT images using radiomics. BMC Medical Imaging. 25(1). 285–285. 1 indexed citations
2.
Deniffel, Dominik, Nathan Perlis, Sangeet Ghai, et al.. (2025). Optimizing biopsy decisions in PI-RADS 3 lesions: cross-institutional validation of a local clinical risk model. World Journal of Urology. 43(1). 253–253. 1 indexed citations
3.
Namdar, Khashayar, Uri Tabori, Cynthia Hawkins, et al.. (2024). Identification of Multiclass Pediatric Low-Grade Neuroepithelial Tumor Molecular Subtype with ADC MR Imaging and Machine Learning. American Journal of Neuroradiology. 45(6). 753–760. 2 indexed citations
4.
Namdar, Khashayar, Matthias Wagner, Cynthia Hawkins, et al.. (2024). Improving Deep Learning Models for Pediatric Low-Grade Glioma Tumours Molecular Subtype Identification Using MRI-based 3D Probability Distributions of Tumour Location. Canadian Association of Radiologists Journal. 76(2). 313–323. 4 indexed citations
5.
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Namdar, Khashayar, et al.. (2024). Deep superpixel generation and clustering for weakly supervised segmentation of brain tumors in MR images. BMC Medical Imaging. 24(1). 335–335. 2 indexed citations
7.
8.
Wagner, Matthias, Liana Nobre, Khashayar Namdar, et al.. (2023). T2-FLAIR Mismatch Sign in Pediatric Low-Grade Glioma. American Journal of Neuroradiology. 44(7). 841–845. 10 indexed citations
9.
Wagner, Matthias, Khashayar Namdar, Liana Nobre, et al.. (2023). Increased confidence of radiomics facilitating pretherapeutic differentiation of BRAF-altered pediatric low-grade glioma. European Radiology. 34(4). 2772–2781. 8 indexed citations
10.
Namdar, Khashayar, et al.. (2023). Automated Adolescence Scoliosis Detection Using Augmented U-Net With Non-square Kernels. Canadian Association of Radiologists Journal. 74(4). 667–675. 8 indexed citations
11.
12.
Namdar, Khashayar, et al.. (2023). Exploring potential barriers in equitable access to pediatric diagnostic imaging using machine learning. Frontiers in Public Health. 11. 968319–968319. 5 indexed citations
13.
Wagner, Matthias, Khashayar Namdar, Nicolin Hainc, et al.. (2022). Radiomic Features Based on MRI Predict Progression-Free Survival in Pediatric Diffuse Midline Glioma/Diffuse Intrinsic Pontine Glioma. Canadian Association of Radiologists Journal. 74(1). 119–126. 21 indexed citations
14.
Namdar, Khashayar, et al.. (2022). Improving disease classification performance and explainability of deep learning models in radiology with heatmap generators. SHILAP Revista de lepidopterología. 2. 991683–991683. 8 indexed citations
15.
Wagner, Matthias, Nicolin Hainc, Farzad Khalvati, et al.. (2021). Radiomics of Pediatric Low-Grade Gliomas: Toward a Pretherapeutic Differentiation ofBRAF-Mutated andBRAF-Fused Tumors. American Journal of Neuroradiology. 42(4). 759–765. 36 indexed citations
16.
Namdar, Khashayar, et al.. (2021). A Comprehensive Study of Data Augmentation Strategies for Prostate Cancer Detection in Diffusion-Weighted MRI Using Convolutional Neural Networks. Journal of Digital Imaging. 34(4). 862–876. 50 indexed citations
17.
Wagner, Matthias, et al.. (2021). Radiomics, machine learning, and artificial intelligence—what the neuroradiologist needs to know. Neuroradiology. 63(12). 1957–1967. 104 indexed citations
18.
Deniffel, Dominik, Nabila Abraham, Khashayar Namdar, et al.. (2020). Using decision curve analysis to benchmark performance of a magnetic resonance imaging–based deep learning model for prostate cancer risk assessment. European Radiology. 30(12). 6867–6876. 25 indexed citations
19.
Namdar, Khashayar, et al.. (2020). A Brief Review of Deep Multi-task Learning and Auxiliary Task Learning. arXiv (Cornell University). 3 indexed citations
20.

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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