Orit Shimon

1.3k citations
15 papers · 931 · 1 hit paper · h-index 10

Impact in

Papers in

Orit Shimon

14 papers receiving 910 citations

Orit Shimon's Hit Papers

Convolutional Neural Networks for Radiologic Images: A Radiologist’s Guide 2019 · 372 citations
3720+2+4Years since publication100200300

Peers

Orit Shimon
Comparison fields: 5 of 124
  • Health Informatics 114
  • Gastroenterology 213
  • Internal Medicine 59
  • Radiology, Nuclear Medicine and Imaging 334
  • Oncology 214
Replace Tomoyuki Fujioka with:
Tomoyuki Fujioka Japan
Andrew S. Wu United States
Michael P. McRae United States
Dan Ionuț Gheonea Romania
Oğuz Dıcle Türkiye
Aymeric Becq France
Felix Hähn Germany
Jae‐Kwang Lim South Korea
Xueqian Xie China
Caroline Taylor United States
Orit Shimon relative to Tomoyuki Fujioka Japan Tomoyuki Fujioka's profile →
Citations per field
00.5×11.8×
Tomoyuki Fujioka · 1×
Citations per year

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-authors

The 25 scholars most cited alongside Orit Shimon, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Orit Shimon Line = papers co-authored together Orit Shimon links everyone, so they are left out of the graph.

All Works

15 of 15 papers shown
#Work
1
Convolutional Neural Networks for Radiologic Images: A Radiologist’s Guide
Hit paper breakdown →
2019372
2 2019169
3 2020152
4 202194
5 202142
6 202126
7 201823
8 202020
9 201913
10 20199
11 20174
12 20184
13 20222
14 20221
15 20190

About Orit Shimon

Orit Shimon is a scholar working on Pulmonary and Respiratory Medicine, Surgery, Epidemiology, Radiology, Nuclear Medicine and Imaging and Cardiology and Cardiovascular Medicine, having authored 15 papers that have together received 931 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (3 papers), Gastrointestinal Bleeding Diagnosis and Treatment (2 papers), Liver Disease Diagnosis and Treatment (2 papers), Sarcoma Diagnosis and Treatment (2 papers), Cardiac tumors and thrombi (2 papers), Hepatocellular Carcinoma Treatment and Prognosis (1 paper), Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis (1 paper) and COVID-19 diagnosis using AI (1 paper). The work is most often cited by research in Health Informatics (114 citations), Gastroenterology (213 citations), Internal Medicine (59 citations), Radiology, Nuclear Medicine and Imaging (334 citations) and Oncology (214 citations). Orit Shimon has collaborated with scholars based in Israel, United States and Switzerland. Frequent co-authors include Eyal Klang, Shelly Soffer, Michal Marianne Amitai, Hayit Greenspan, Avi Ben-Cohen, Yiftach Barash, Uri Kopylov, Shomron Ben‐Horin, Rami Eliakim and Eli Konen. Their work appears in journals such as Gastrointestinal Endoscopy, Liver International, Neuroradiology, Scientific Reports and Acta Anaesthesiologica Scandinavica.

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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