Saba Dadsetan

445 total citations · 1 hit paper
6 papers, 260 citations indexed

About

Saba Dadsetan is a scholar working on Artificial Intelligence, Oncology and Media Technology. According to data from OpenAlex, Saba Dadsetan has authored 6 papers receiving a total of 260 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Artificial Intelligence, 3 papers in Oncology and 2 papers in Media Technology. Recurrent topics in Saba Dadsetan's work include AI in cancer detection (3 papers), Radiomics and Machine Learning in Medical Imaging (2 papers) and Colorectal Cancer Screening and Detection (2 papers). Saba Dadsetan is often cited by papers focused on AI in cancer detection (3 papers), Radiomics and Machine Learning in Medical Imaging (2 papers) and Colorectal Cancer Screening and Detection (2 papers). Saba Dadsetan collaborates with scholars based in United States and United Kingdom. Saba Dadsetan's co-authors include Kris Kitani, S. Alireza Golestaneh, Margarita L. Zuley, Dooman Arefan, Jules H. Sumkin, Shandong Wu, Wendie A. Berg, Naira Hovakimyan, Min Sun and Heng Huang and has published in prestigious journals such as Pattern Recognition, Lecture notes in computer science and arXiv (Cornell University).

In The Last Decade

Saba Dadsetan

6 papers receiving 250 citations

Hit Papers

align trajectories

What are hit papers?

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.

No-Reference Image Quality Assessment via Transformers, Relative Ranking, and Self-Consistency

2022 220 citations S. Alireza Golestaneh, Saba Dadsetan et al. 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Saba Dadsetan United States	4	190	127	38	29	21	6	260
Tianhe Wu United States	4	191 1.0×	107 0.8×	15 0.4×	18 0.6×	14 0.7×	7	279
Shaolin Su China	5	446 2.3×	236 1.9×	23 0.6×	16 0.6×	31 1.5×	12	518
Praful Gupta United States	8	418 2.2×	193 1.5×	13 0.3×	19 0.7×	15 0.7×	15	469
S. Alireza Golestaneh United States	6	432 2.3×	287 2.3×	22 0.6×	15 0.5×	27 1.3×	10	483
Hao-Hsiang Yang Taiwan	8	276 1.5×	117 0.9×	13 0.3×	14 0.5×	6 0.3×	15	325
Magudeeswaran Veluchamy India	13	391 2.1×	233 1.8×	37 1.0×	42 1.4×	9 0.4×	24	465
Jiahao Wang China	4	159 0.8×	81 0.6×	14 0.4×	12 0.4×	9 0.4×	11	227
Geet Sahu India	9	262 1.4×	151 1.2×	22 0.6×	28 1.0×	4 0.2×	17	359
Jun Lyu China	9	159 0.8×	83 0.7×	41 1.1×	60 2.1×	4 0.2×	27	281
Hossein Ziaei Nafchi Canada	9	286 1.5×	163 1.3×	25 0.7×	11 0.4×	11 0.5×	13	349

Countries citing papers authored by Saba Dadsetan

Since Specialization

Citations

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

Fields of papers citing papers by Saba Dadsetan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Saba Dadsetan

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

All Works

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6 of 6 papers shown

1.

Dadsetan, Saba, Dooman Arefan, Wendie A. Berg, et al.. (2022). Deep learning of longitudinal mammogram examinations for breast cancer risk prediction. Pattern Recognition. 132. 108919–108919. 27 indexed citations

2.

Golestaneh, S. Alireza, Saba Dadsetan, & Kris Kitani. (2022). No-Reference Image Quality Assessment via Transformers, Relative Ranking, and Self-Consistency. 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). 3989–3999. 220 indexed citations breakdown →

3.

Dadsetan, Saba, Dooman Arefan, Margarita L. Zuley, et al.. (2021). Radiomics-Informed Deep Curriculum Learning for Breast Cancer Diagnosis. Lecture notes in computer science. 12905. 634–643. 4 indexed citations

4.

Dadsetan, Saba, Dooman Arefan, Margarita L. Zuley, et al.. (2021). Learning knowledge from longitudinal data of mammograms to improving breast cancer risk prediction. 22–22. 3 indexed citations

5.

Dadsetan, Saba, et al.. (2020). Detection and Prediction of Nutrient Deficiency Stress using Longitudinal Aerial Imagery. arXiv (Cornell University). 5 indexed citations

6.

Dadsetan, Saba & Shandong Wu. (2019). A data interpretation approach for deep learning-based prediction models. 1341. 21–21. 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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