Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
The applications of machine learning techniques in medical data processing based on distributed computing and the Internet of Things
2023105 citationsSarina Aminizadeh, Arash Heidari et al.Computer Methods and Programs in Biomedicineprofile →
Opportunities and challenges of artificial intelligence and distributed systems to improve the quality of healthcare service
2024101 citationsSarina Aminizadeh, Arash Heidari et al.Artificial Intelligence in Medicineprofile →
A new lung cancer detection method based on the chest CT images using Federated Learning and blockchain systems
202380 citationsArash Heidari, Danial Javaheri et al.Artificial Intelligence in Medicineprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Mahsa Rezaei'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 Mahsa Rezaei with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mahsa Rezaei more than expected).
This network shows the impact of papers produced by Mahsa Rezaei. 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 Mahsa Rezaei. The network helps show where Mahsa Rezaei may publish in the future.
Co-authorship network of co-authors of Mahsa Rezaei
This figure shows the co-authorship network connecting the top 25 collaborators of Mahsa Rezaei.
A scholar is included among the top collaborators of Mahsa Rezaei 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 Mahsa Rezaei. Mahsa Rezaei is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
6 of 6 papers shown
1.
Aminizadeh, Sarina, Arash Heidari, Mahshid Dehghan, et al.. (2024). Opportunities and challenges of artificial intelligence and distributed systems to improve the quality of healthcare service. Artificial Intelligence in Medicine. 149. 102779–102779.101 indexed citations breakdown →
2.
Aminizadeh, Sarina, Arash Heidari, Shiva Toumaj, et al.. (2023). The applications of machine learning techniques in medical data processing based on distributed computing and the Internet of Things. Computer Methods and Programs in Biomedicine. 241. 107745–107745.105 indexed citations breakdown →
3.
Heidari, Arash, Danial Javaheri, Shiva Toumaj, et al.. (2023). A new lung cancer detection method based on the chest CT images using Federated Learning and blockchain systems. Artificial Intelligence in Medicine. 141. 102572–102572.80 indexed citations breakdown →
Farhoudi, Mehdi, Homayoun Sadeghi‐Bazargani, Mazyar Hashemilar, et al.. (2017). Stroke subtypes, risk factors and mortality rate in northwest of Iran.. PubMed. 16(3). 112–117.19 indexed citations
6.
Rezaei, Mahsa, et al.. (2015). Google Cardboard anterior and posterior segment imaging: a valuable tool for limited-resource settings. Investigative Ophthalmology & Visual Science. 56(7). 4101–4101.1 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.