Rasoul Sali

587 total citations
11 papers, 349 citations indexed

About

Rasoul Sali is a scholar working on Artificial Intelligence, Oncology and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Rasoul Sali has authored 11 papers receiving a total of 349 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 3 papers in Oncology and 3 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Rasoul Sali's work include AI in cancer detection (6 papers), Radiomics and Machine Learning in Medical Imaging (3 papers) and Colorectal Cancer Screening and Detection (3 papers). Rasoul Sali is often cited by papers focused on AI in cancer detection (6 papers), Radiomics and Machine Learning in Medical Imaging (3 papers) and Colorectal Cancer Screening and Detection (3 papers). Rasoul Sali collaborates with scholars based in United States, Iran and Jordan. Rasoul Sali's co-authors include Iman Ramezani, Milad Dehghani, Mojtaba Khorram Niaki, Hassan Shavandi, Masoumeh Sadeghi, Sana Syed, Donald E. Brown, Lubaina Ehsan, Zhiheng Pei and Nizal Sarrafzadegan and has published in prestigious journals such as Nature Communications, JNCI Journal of the National Cancer Institute and Scientific Reports.

In The Last Decade

Rasoul Sali

10 papers receiving 331 citations

Peers

Rasoul Sali

SPS

MARKE

ISM

AI

RNMI

Erkan Akar Türkiye

Jie Cai United States

Ming‐Yi Chen Taiwan

Young A Park South Korea

So Young Lee South Korea

Jonas Hoffmann France

Congcong Zhang China

Jiaolong Xue China

Hua Fan China

Anja Geigenmüller Germany

Erkan Akar Türkiye

Rasoul Sali

254 ×1.4

SPS

104 ×0.9

MARKE

123 ×1.5

ISM

27 ×0.5

AI

4 ×0.1

RNMI

Citations per year, relative to Rasoul Sali Rasoul Sali (= 1×) peers Erkan Akar

Countries citing papers authored by Rasoul Sali

Since Specialization

Citations

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

Fields of papers citing papers by Rasoul Sali

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Rasoul Sali

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

All Works

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11 of 11 papers shown

1.

Sali, Rasoul, Feyisope Eweje, Xiaoming Zhang, et al.. (2025). Automated cell annotation and classification on histopathology for spatial biomarker discovery. Nature Communications. 16(1). 6240–6240.

2.

Sali, Rasoul, et al.. (2024). Implementation of Digital Pathology and Artificial Intelligence in Routine Pathology Practice. Laboratory Investigation. 104(9). 102111–102111. 22 indexed citations

3.

Sali, Rasoul, et al.. (2023). Morphological diversity of cancer cells predicts prognosis across tumor types. JNCI Journal of the National Cancer Institute. 116(4). 555–564. 4 indexed citations

4.

Sali, Rasoul, et al.. (2023). Deep Feature Learning for Microwave Mammography with Convolutional Autoencoders. 307–308. 1 indexed citations

5.

Yu, Feiyang, Xi Wang, Rasoul Sali, & Ruijiang Li. (2023). Single-Cell Heterogeneity-Aware Transformer-Guided Multiple Instance Learning for Cancer Aneuploidy Prediction From Whole Slide Histopathology Images. IEEE Journal of Biomedical and Health Informatics. 28(1). 134–144. 7 indexed citations

6.

Syed, Sana, Lubaina Ehsan, Marium Khan, et al.. (2021). Artificial Intelligence‐based Analytics for Diagnosis of Small Bowel Enteropathies and Black Box Feature Detection. Journal of Pediatric Gastroenterology and Nutrition. 72(6). 833–841. 8 indexed citations

7.

Shah, Tilak, Lubaina Ehsan, Rasoul Sali, et al.. (2021). Deep learning systems detect dysplasia with human-like accuracy using histopathology and probe-based confocal laser endomicroscopy. Scientific Reports. 11(1). 5086–5086. 19 indexed citations

8.

Sali, Rasoul, et al.. (2020). Deep Learning for Whole-Slide Tissue Histopathology Classification: A Comparative Study in the Identification of Dysplastic and Non-Dysplastic Barrett’s Esophagus. Journal of Personalized Medicine. 10(4). 141–141. 22 indexed citations

9.

Sali, Rasoul, Hassan Shavandi, & Masoumeh Sadeghi. (2016). A clinical decision support system based on support vector machine and binary particle swarm optimisation for cardiovascular disease diagnosis. International Journal of Data Mining and Bioinformatics. 15(4). 312–312. 15 indexed citations

10.

Dehghani, Milad, Mojtaba Khorram Niaki, Iman Ramezani, & Rasoul Sali. (2016). Evaluating the influence of YouTube advertising for attraction of young customers. Computers in Human Behavior. 59. 165–172. 230 indexed citations

11.

Sali, Rasoul, et al.. (2013). Validation of the Revised Stressful Life Event Questionnaire Using a Hybrid Model of Genetic Algorithm and Artificial Neural Networks. Computational and Mathematical Methods in Medicine. 2013. 1–7. 21 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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