Dror Lederman

651 citations
37 papers · 490 · h-index 14

Impact in

Papers in

Dror Lederman

36 papers receiving 465 citations

Peers

Dror Lederman
Comparison fields: 5 of 78
  • Pharmacy 71
  • Artificial Intelligence 247
  • Radiology, Nuclear Medicine and Imaging 132
  • Signal Processing 49
  • Pulmonary and Respiratory Medicine 115
Replace Tomás Teijeiro with:
Tomás Teijeiro Switzerland
Hua-Nong Ting Malaysia
Muntasir Mamun United States
Alejandro A. Torres-García Mexico
Valérie Louis-Dorr France
Amir Tahmasebi United States
Rumana Islam Canada
Maymouna Ezeddin Qatar
Kyle Hasenstab United States
Hela Mahersia Tunisia
Dror Lederman relative to Tomás Teijeiro Switzerland Tomás Teijeiro's profile →
Citations per field
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Tomás Teijeiro · 1×
Citations per year

Countries citing papers authored by Dror Lederman

Since Specialization
Citations

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

Fields of papers citing papers by Dror Lederman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Dror Lederman, 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 Dror Lederman Line = papers co-authored together Dror Lederman links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 37 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201154
2 201150
3 201832
4 201029
5 200828
6 201028
7 200327
8 201223
9 201022
10 201119
11
AUTOMATIC CLASSIFICATION OF INFANTS ’ CRY
200218
12 200616
13 201216
14 201816
15 201112
16 202111
17 202410
18 20189
19 20109
20 20129

About Dror Lederman

Dror Lederman is a scholar working on Artificial Intelligence, Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition and Anesthesiology and Pain Medicine, having authored 37 papers that have together received 490 indexed citations. Recurring topics across this work include AI in cancer detection (14 papers), Digital Radiography and Breast Imaging (6 papers), Radiomics and Machine Learning in Medical Imaging (4 papers), Airway Management and Intubation Techniques (4 papers), Image Retrieval and Classification Techniques (4 papers), Obstructive Sleep Apnea Research (3 papers), Electrical and Bioimpedance Tomography (3 papers) and Lung Cancer Diagnosis and Treatment (3 papers). The work is most often cited by research in Pharmacy (71 citations), Artificial Intelligence (247 citations), Radiology, Nuclear Medicine and Imaging (132 citations), Signal Processing (49 citations) and Pulmonary and Respiratory Medicine (115 citations). Dror Lederman has collaborated with scholars based in United States, Israel and China. Frequent co-authors include Bin Zheng, Xingwei Wang, Jun Tan, Xiao Hui Wang, David Gur, Joseph Tabrikian, Yaniv Zigel, Ehud Zmora, Angelika Stellzig‐Eisenhauer and Kathleen Wermke. Their work appears in journals such as Academic Radiology, Medical Physics, Annals of Biomedical Engineering, Anesthesia & Analgesia and Physics in Medicine and Biology.

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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