Dariush Askari

528 total citations
7 papers, 229 citations indexed

About

Dariush Askari is a scholar working on Radiology, Nuclear Medicine and Imaging, Biomedical Engineering and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Dariush Askari has authored 7 papers receiving a total of 229 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Radiology, Nuclear Medicine and Imaging, 5 papers in Biomedical Engineering and 2 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Dariush Askari's work include Advanced X-ray and CT Imaging (5 papers), Radiomics and Machine Learning in Medical Imaging (4 papers) and Medical Imaging Techniques and Applications (3 papers). Dariush Askari is often cited by papers focused on Advanced X-ray and CT Imaging (5 papers), Radiomics and Machine Learning in Medical Imaging (4 papers) and Medical Imaging Techniques and Applications (3 papers). Dariush Askari collaborates with scholars based in Iran, Switzerland and Netherlands. Dariush Askari's co-authors include Habib Zaidi, Isaac Shiri, Hossein Arabi, Yazdan Salimi, Hamid Abdollahi, Azadeh Akhavanallaf, Amirhossein Sanaat, Saleh Sandoughdaran, Ghasem Hajianfar and Kiara Rezaei‐Kalantari and has published in prestigious journals such as European Radiology, Computers in Biology and Medicine and Insights into Imaging.

In The Last Decade

Dariush Askari

7 papers receiving 227 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Dariush Askari Iran	5	197	88	40	34	31	7	229
Chengzhu Zhang United States	6	229 1.2×	125 1.4×	37 0.9×	17 0.5×	42 1.4×	12	291
Xin Tie China	9	184 0.9×	32 0.4×	57 1.4×	33 1.0×	58 1.9×	24	264
Sajad P. Shayesteh Iran	6	219 1.1×	77 0.9×	39 1.0×	41 1.2×	21 0.7×	9	240
Ivan A. Blokhin Russia	8	165 0.8×	39 0.4×	42 1.1×	48 1.4×	44 1.4×	44	227
Emi Yamaga Japan	9	225 1.1×	32 0.4×	152 3.8×	46 1.4×	25 0.8×	28	311
Valeria Chernina Russia	6	136 0.7×	29 0.3×	39 1.0×	47 1.4×	34 1.1×	21	175
Hadi Karimi Mobin Iran	6	116 0.6×	37 0.4×	24 0.6×	24 0.7×	18 0.6×	6	136
Michail Mamalakis United Kingdom	9	157 0.8×	37 0.4×	70 1.8×	32 0.9×	37 1.2×	19	245
Shravya Shetty United States	7	167 0.8×	35 0.4×	69 1.7×	66 1.9×	75 2.4×	15	276
Xiangguang Chen China	7	196 1.0×	32 0.4×	48 1.2×	67 2.0×	14 0.5×	16	277

Countries citing papers authored by Dariush Askari

Since Specialization

Citations

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

Fields of papers citing papers by Dariush Askari

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dariush Askari

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

All Works

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7 of 7 papers shown

1.

Hosseinzadeh, Mahdi, Dariush Askari, Ghasem Hajianfar, et al.. (2022). Prediction of TNM stage in head and neck cancer using hybrid machine learning systems and radiomics features. 114–114. 19 indexed citations

2.

Hosseinzadeh, Mahdi, et al.. (2022). Advanced survival prediction in head and neck cancer using hybrid machine learning systems and radiomics features. 45–45. 3 indexed citations

3.

Salimi, Yazdan, Isaac Shiri, Azadeh Akhavanallaf, et al.. (2021). Deep learning-based fully automated Z-axis coverage range definition from scout scans to eliminate overscanning in chest CT imaging. Insights into Imaging. 12(1). 162–162. 37 indexed citations

4.

Shiri, Isaac, Majid Sorouri, Parham Geramifar, et al.. (2021). Machine learning-based prognostic modeling using clinical data and quantitative radiomic features from chest CT images in COVID-19 patients. Computers in Biology and Medicine. 132. 104304–104304. 72 indexed citations

5.

Shiri, Isaac, Hossein Arabi, Yazdan Salimi, et al.. (2021). COLI‐Net : Deep learning‐assisted fully automated COVID ‐19 lung and infection pneumonia lesion detection and segmentation from chest computed tomography images. International Journal of Imaging Systems and Technology. 32(1). 12–25. 27 indexed citations

6.

Shiri, Isaac, Azadeh Akhavanallaf, Amirhossein Sanaat, et al.. (2020). Ultra-low-dose chest CT imaging of COVID-19 patients using a deep residual neural network. European Radiology. 31(3). 1420–1431. 70 indexed citations

7.

Shiri, Isaac, Azadeh Akhavanallaf, Amirhossein Sanaat, et al.. (2020). Deep Residual Neural Network-based Standard CT Estimation from Ultra-Low Dose CT Imaging for COVID-19 Patients. 1–3. 1 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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