Sami Tabbarah

666 total citations
9 papers, 190 citations indexed

About

Sami Tabbarah is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Oncology. According to data from OpenAlex, Sami Tabbarah has authored 9 papers receiving a total of 190 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Radiology, Nuclear Medicine and Imaging, 4 papers in Artificial Intelligence and 3 papers in Oncology. Recurrent topics in Sami Tabbarah's work include Radiomics and Machine Learning in Medical Imaging (6 papers), AI in cancer detection (4 papers) and Medical Imaging Techniques and Applications (3 papers). Sami Tabbarah is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (6 papers), AI in cancer detection (4 papers) and Medical Imaging Techniques and Applications (3 papers). Sami Tabbarah collaborates with scholars based in Canada, United Kingdom and United States. Sami Tabbarah's co-authors include Andrew Lagree, William T. Tran, Ali Sadeghi‐Naini, Tina Wu, Katarzyna J. Jerzak, Jonathan Klein, Fang‐I Lu, Iván M. Rosado-Méndez, Fang-I Lu and David W. Dodington and has published in prestigious journals such as Scientific Reports, International Journal of Radiation Oncology*Biology*Physics and Breast Cancer Research and Treatment.

In The Last Decade

Sami Tabbarah

9 papers receiving 186 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sami Tabbarah Canada 6 142 100 44 25 25 9 190
Andrew Lagree Canada 11 192 1.4× 151 1.5× 61 1.4× 52 2.1× 29 1.2× 15 295
Ran Gu China 12 136 1.0× 122 1.2× 49 1.1× 52 2.1× 46 1.8× 30 328
Paula Toro United States 8 125 0.9× 92 0.9× 37 0.8× 58 2.3× 18 0.7× 27 239
Daniel DiCenzo Canada 11 177 1.2× 89 0.9× 28 0.6× 23 0.9× 57 2.3× 18 209
Zhi-Rui Chuan China 4 250 1.8× 152 1.5× 61 1.4× 38 1.5× 30 1.2× 6 293
Xiaofeng Jiang China 7 188 1.3× 63 0.6× 33 0.8× 111 4.4× 35 1.4× 13 285
Lizhi Shao China 9 174 1.2× 72 0.7× 33 0.8× 40 1.6× 25 1.0× 28 244
Peter Truszkowski United States 2 187 1.3× 211 2.1× 41 0.9× 60 2.4× 22 0.9× 2 341
Eduardo Pascual Van Sant United States 7 252 1.8× 208 2.1× 60 1.4× 38 1.5× 22 0.9× 9 314
Gaia Ninatti Italy 8 182 1.3× 49 0.5× 24 0.5× 37 1.5× 51 2.0× 18 249

Countries citing papers authored by Sami Tabbarah

Since Specialization
Citations

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

Fields of papers citing papers by Sami Tabbarah

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sami Tabbarah

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

All Works

9 of 9 papers shown
1.
Ferré, Romuald, Andrew Lagree, Sami Tabbarah, et al.. (2023). Machine learning analysis of breast ultrasound to classify triple negative and HER2+ breast cancer subtypes. Breast Disease. 42(1). 59–66. 19 indexed citations
2.
Dodington, David W., Andrew Lagree, Sami Tabbarah, et al.. (2021). Analysis of tumor nuclear features using artificial intelligence to predict response to neoadjuvant chemotherapy in high-risk breast cancer patients. Breast Cancer Research and Treatment. 186(2). 379–389. 26 indexed citations
3.
Meti, Nicholas, Andrew Lagree, Sami Tabbarah, et al.. (2021). Machine Learning Frameworks to Predict Neoadjuvant Chemotherapy Response in Breast Cancer Using Clinical and Pathological Features. JCO Clinical Cancer Informatics. 5(5). 66–80. 31 indexed citations
4.
Tabbarah, Sami, Erika Tavares, Jason Charish, et al.. (2020). COG5 variants lead to complex early onset retinal degeneration, upregulation of PERK and DNA damage. Scientific Reports. 10(1). 21269–21269. 2 indexed citations
5.
Tabbarah, Sami, Andrew Lagree, Tina Wu, et al.. (2020). Quantitative Thermal Imaging Biomarkers to Detect Acute Skin Toxicity From Breast Radiation Therapy Using Supervised Machine Learning. International Journal of Radiation Oncology*Biology*Physics. 106(5). 1071–1083. 28 indexed citations
6.
Wu, Tina, Sami Tabbarah, Andrew Lagree, & William T. Tran. (2020). Predictive Models for Neoadjuvant Chemotherapy Response in Breast Cancer Patients Using Quantitative Digital Pathology Imaging Biomarkers. Journal of medical imaging and radiation sciences. 51(3). S12–S12. 2 indexed citations
7.
Lin, Victor S.‐Y., Tina Wu, E. Garcı́a, et al.. (2019). Quantitative Thermal Imaging Using Grey-level Run Length Matrix Texture Features Correlate to Radiation-Induced Skin Toxicity. Journal of medical imaging and radiation sciences. 50(2). S6–S7. 4 indexed citations
8.
Tran, William T., Katarzyna J. Jerzak, Fang-I Lu, et al.. (2019). Personalized Breast Cancer Treatments Using Artificial Intelligence in Radiomics and Pathomics. Journal of medical imaging and radiation sciences. 50(4). S32–S41. 60 indexed citations
9.
Tran, William T., Irene Karam, Ian Poon, et al.. (2019). Predictive Quantitative Ultrasound Radiomic Markers Associated With Treatment Response in Head and Neck Cancer. Future Science OA. 6(1). FSO433–FSO433. 18 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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