Dooman Arefan

1.0k total citations
43 papers, 597 citations indexed

About

Dooman Arefan is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Dooman Arefan has authored 43 papers receiving a total of 597 indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Radiology, Nuclear Medicine and Imaging, 22 papers in Artificial Intelligence and 12 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Dooman Arefan's work include AI in cancer detection (20 papers), Radiomics and Machine Learning in Medical Imaging (19 papers) and MRI in cancer diagnosis (9 papers). Dooman Arefan is often cited by papers focused on AI in cancer detection (20 papers), Radiomics and Machine Learning in Medical Imaging (19 papers) and MRI in cancer diagnosis (9 papers). Dooman Arefan collaborates with scholars based in United States, China and Iran. Dooman Arefan's co-authors include Shandong Wu, Margarita L. Zuley, Jules H. Sumkin, Wendie A. Berg, Ruimei Chai, Min Sun, Aly A. Mohamed, Qian Zhang, He Ma and Alireza Talebpour and has published in prestigious journals such as Nature Communications, Radiology and Pattern Recognition.

In The Last Decade

Dooman Arefan

38 papers receiving 582 citations

Peers

Dooman Arefan

RNMI

PRM

ONCOL

Yixin Hu China

Ge-Ge Wu China

Fadila Zerka Netherlands

Yini Huang China

Rushuang Mao China

Junming Jian China

Christoph Haarburger Germany

Xingyu Zhao China

Alexander Ciritsis Switzerland

Pritam Mukherjee United States

Yixin Hu China

Dooman Arefan

400 ×1.0

RNMI

239 ×0.9

83 ×0.6

PRM

60 ×0.8

ONCOL

75 ×1.3

Citations per year, relative to Dooman Arefan Dooman Arefan (= 1×) peers Yixin Hu

Countries citing papers authored by Dooman Arefan

Since Specialization

Citations

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

Fields of papers citing papers by Dooman Arefan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dooman Arefan

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

All Works

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20 of 20 papers shown

Kirschen, Matthew P., Jonathan Elmer, Dooman Arefan, et al.. (2025). Machine learning to identify hypoxic-ischemic brain injury on early head CT after pediatric cardiac arrest. Resuscitation. 215. 110693–110693.

Patel, Priya, et al.. (2025). A Radiomic-Clinical Model of Contrast-Enhanced Mammography for Breast Cancer Biopsy Outcome Prediction. Academic Radiology. 32(5). 2438–2449. 2 indexed citations

Arefan, Dooman, et al.. (2024). Incorporating longitudinal changes of mammograms for breast cancer diagnosis. 74–74.

Arefan, Dooman, et al.. (2024). Assessment of Background Parenchymal Enhancement at Dynamic Contrast-enhanced MRI in Predicting Breast Cancer Recurrence Risk. Radiology. 310(1). e230269–e230269. 4 indexed citations

Arefan, Dooman, et al.. (2024). Knowledge-Guided Multi-Task Learning for Breast Cancer Diagnosis Using Longitudinal Mammogram Images. 1–5.

Sun, Min, et al.. (2024). Deep learning of mammogram images to reduce unnecessary breast biopsies: a preliminary study. Breast Cancer Research. 26(1). 82–82. 4 indexed citations

Arefan, Dooman, Roberta Catania, Akshata Moghe, et al.. (2024). Quantitative radiomics and qualitative LI-RADS imaging descriptors for non-invasive assessment of β-catenin mutation status in hepatocellular carcinoma. Abdominal Radiology. 49(7). 2220–2230. 5 indexed citations

Arefan, Dooman, Marie A. Ganott, Amy E. Kelly, et al.. (2023). Contrast-enhanced Mammography-guided Biopsy: Initial Trial and Experience. Journal of Breast Imaging. 5(2). 148–158. 12 indexed citations

Arefan, Dooman, Matthew Pease, Shawn R. Eagle, David O. Okonkwo, & Shandong Wu. (2023). Comparison of machine learning models to predict long-term outcomes after severe traumatic brain injury. Neurosurgical FOCUS. 54(6). E14–E14. 7 indexed citations

10.

Elmer, Jonathan, et al.. (2023). Interpretable machine learning model for imaging-based outcome prediction after cardiac arrest. Resuscitation. 191. 109894–109894. 3 indexed citations

11.

Liu, Chang, et al.. (2023). Medical knowledge-guided deep learning for mammographic breast density classification. 115–115. 1 indexed citations

12.

Luo, Jun, et al.. (2023). Human Not in the Loop: Objective Sample Difficulty Measures for Curriculum Learning. PubMed. 2023. 1–5. 1 indexed citations

13.

Elmer, Jonathan, Matthew Pease, Dooman Arefan, et al.. (2022). Deep learning of early brain imaging to predict post-arrest electroencephalography. Resuscitation. 172. 17–23. 6 indexed citations

14.

Yang, Lü, et al.. (2022). A CT-based radiomics model for predicting renal capsule invasion in renal cell carcinoma. BMC Medical Imaging. 22(1). 15–15. 14 indexed citations

15.

Li, Qiong, Liang Qi, Xi-Sheng Liu, et al.. (2022). Identifying Prognostic Markers From Clinical, Radiomics, and Deep Learning Imaging Features for Gastric Cancer Survival Prediction. Frontiers in Oncology. 11. 725889–725889. 15 indexed citations

16.

Li, Huanhuan, Long Gao, He Ma, et al.. (2021). Radiomics-Based Features for Prediction of Histological Subtypes in Central Lung Cancer. Frontiers in Oncology. 11. 658887–658887. 27 indexed citations

17.

Zhou, Qianwei, Margarita L. Zuley, Yuan Guo, et al.. (2021). A machine and human reader study on AI diagnosis model safety under attacks of adversarial images. Nature Communications. 12(1). 7281–7281. 32 indexed citations

18.

Arefan, Dooman, Ryan Hausler, Jules H. Sumkin, Min Sun, & Shandong Wu. (2021). Predicting cell invasion in breast tumor microenvironment from radiological imaging phenotypes. BMC Cancer. 21(1). 370–370. 23 indexed citations

19.

Arefan, Dooman, et al.. (2018). Calculation of the contrast of the calcification in digital mammography system. Journal of Cancer Research and Therapeutics. 14(2). 335–340. 2 indexed citations

20.

Talebpour, Alireza, Dooman Arefan, & Hamid Mohamadlou. (2013). Automated Abnormal Mass Detection in the Mammogram Images Using Chebyshev Moments. Research Journal of Applied Sciences Engineering and Technology. 5(2). 513–518. 6 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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