Orit Shimon

1.3k total citations · 1 hit paper
15 papers, 931 citations indexed

About

Orit Shimon is a scholar working on Pulmonary and Respiratory Medicine, Surgery and Epidemiology. According to data from OpenAlex, Orit Shimon has authored 15 papers receiving a total of 931 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Pulmonary and Respiratory Medicine, 3 papers in Surgery and 3 papers in Epidemiology. Recurrent topics in Orit Shimon's work include Radiomics and Machine Learning in Medical Imaging (3 papers), Liver Disease Diagnosis and Treatment (2 papers) and Cardiac tumors and thrombi (2 papers). Orit Shimon is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (3 papers), Liver Disease Diagnosis and Treatment (2 papers) and Cardiac tumors and thrombi (2 papers). Orit Shimon collaborates with scholars based in Israel, United States and Switzerland. Orit Shimon's co-authors include Eyal Klang, Shelly Soffer, Hayit Greenspan, Avi Ben-Cohen, Michal Marianne Amitai, Yiftach Barash, Uri Kopylov, Shomron Ben‐Horin, Rami Eliakim and Eli Konen and has published in prestigious journals such as Scientific Reports, Radiology and Gastrointestinal Endoscopy.

In The Last Decade

Orit Shimon

14 papers receiving 910 citations

Hit Papers

Convolutional Neural Networks for Radiologic Images: A Ra... 2019 2026 2021 2023 2019 100 200 300
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.

  1. Convolutional Neural Networks for Radiologic Images: A Radiologist’s Guide
    2019 372 citations Shelly Soffer, Avi Ben-Cohen et al. Radiology profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Orit Shimon Israel 10 334 230 214 213 175 15 931
Tomoyuki Fujioka Japan 22 874 2.6× 161 0.7× 92 0.4× 42 0.2× 202 1.2× 94 1.5k
Dan Ionuț Gheonea Romania 19 314 0.9× 356 1.5× 727 3.4× 86 0.4× 600 3.4× 113 1.4k
Oğuz Dıcle Türkiye 19 319 1.0× 134 0.6× 122 0.6× 33 0.2× 247 1.4× 75 1.0k
Tudorel Ciurea Romania 18 387 1.2× 372 1.6× 785 3.7× 127 0.6× 677 3.9× 86 1.4k
Rui Ji China 16 136 0.4× 452 2.0× 325 1.5× 142 0.7× 491 2.8× 69 884
Aymeric Becq France 14 317 0.9× 511 2.2× 530 2.5× 163 0.8× 377 2.2× 43 1.0k
Hae Yeon Kang South Korea 16 118 0.4× 521 2.3× 308 1.4× 157 0.7× 410 2.3× 41 908
Talal Alkayali United States 5 243 0.7× 197 0.9× 347 1.6× 51 0.2× 85 0.5× 23 555
Xavier Dray France 14 296 0.9× 148 0.6× 474 2.2× 164 0.8× 242 1.4× 42 1.2k
Zixing Huang China 22 743 2.2× 311 1.4× 300 1.4× 26 0.1× 225 1.3× 100 1.6k

Countries citing papers authored by Orit Shimon

Since Specialization
Citations

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

Fields of papers citing papers by Orit Shimon

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Orit Shimon

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

All Works

15 of 15 papers shown
1.
Orbach‐Zinger, Sharon, et al.. (2022). Flouroscopic versus conventional epidural blood patch in obstetrics: A retrospective cohort study. Acta Anaesthesiologica Scandinavica. 66(5). 563–568. 1 indexed citations
2.
Klang, Eyal, Shelly Soffer, Orit Shimon, et al.. (2022). Research trends analysis of chronic hepatitis C versus nonalcoholic fatty liver disease: A literature review text‐mining analysis of publications. Health Science Reports. 5(6). e805–e805. 2 indexed citations
3.
Soffer, Shelly, Adam S. Morgenthau, Orit Shimon, et al.. (2021). Artificial Intelligence for Interstitial Lung Disease Analysis on Chest Computed Tomography: A Systematic Review. Academic Radiology. 29. S226–S235. 42 indexed citations
4.
Soffer, Shelly, et al.. (2021). Deep learning for pulmonary embolism detection on computed tomography pulmonary angiogram: a systematic review and meta-analysis. Scientific Reports. 11(1). 15814–15814. 94 indexed citations
5.
Anteby, Roi, Eyal Klang, Nir Horesh, et al.. (2021). Deep learning for noninvasive liver fibrosis classification: A systematic review. Liver International. 41(10). 2269–2278. 26 indexed citations
6.
Barash, Yiftach, Noam Tau, Shelly Soffer, et al.. (2020). Comparison of deep learning models for natural language processing-based classification of non-English head CT reports. Neuroradiology. 62(10). 1247–1256. 20 indexed citations
7.
Soffer, Shelly, Eyal Klang, Orit Shimon, et al.. (2020). Deep learning for wireless capsule endoscopy: a systematic review and meta-analysis. Gastrointestinal Endoscopy. 92(4). 831–839.e8. 152 indexed citations
8.
Klang, Eyal, Yiftach Barash, Shelly Soffer, et al.. (2019). Deep learning algorithms for automated detection of Crohn’s disease ulcers by video capsule endoscopy. Gastrointestinal Endoscopy. 91(3). 606–613.e2. 169 indexed citations
9.
Soffer, Shelly, Marianne Michal Amitai, Orit Shimon, et al.. (2019). Rhabdomyosarcoma disease spread evaluation on CT scans: Association with primary tumor size and Ki-67 proliferation marker. Clinical Imaging. 56. 41–46. 9 indexed citations
10.
Soffer, Shelly, Marianne Michal Amitai, Iris Barshack, et al.. (2019). PET-CT imaging features that differentiate between positive and negative EWSR1 translocation in Ewing sarcoma. Nuclear Medicine Communications. 40(8). 827–834.
11.
Soffer, Shelly, Avi Ben-Cohen, Orit Shimon, et al.. (2019). Convolutional Neural Networks for Radiologic Images: A Radiologist’s Guide. Radiology. 290(3). 590–606. 372 indexed citations breakdown →
12.
Dickstein, Yaakov, Yonatan Oster, Orit Shimon, et al.. (2019). Antibiotic treatment for invasive nonpregnancy-associated listeriosis and mortality: a retrospective cohort study. European Journal of Clinical Microbiology & Infectious Diseases. 38(12). 2243–2251. 13 indexed citations
13.
14.
Shimon, Orit, Hefziba Green, Noa Eliakim‐Raz, et al.. (2018). Gram-negative bloodstream infections in hemodialysis patients: A retrospective study. Clinical Nephrology. 90(2). 117–124. 4 indexed citations
15.
Klang, Eyal, et al.. (2017). CT imaging features of symptomatic and asymptomatic floating aortic thrombus. Clinical Radiology. 73(3). 323.e9–323.e14. 4 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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