Sinan Onogur

783 total citations
2 papers, 9 citations indexed

About

Sinan Onogur is a scholar working on Surgery, Pulmonary and Respiratory Medicine and Computer Vision and Pattern Recognition. According to data from OpenAlex, Sinan Onogur has authored 2 papers receiving a total of 9 indexed citations (citations by other indexed papers that have themselves been cited), including 1 paper in Surgery, 1 paper in Pulmonary and Respiratory Medicine and 1 paper in Computer Vision and Pattern Recognition. Recurrent topics in Sinan Onogur's work include Radiomics and Machine Learning in Medical Imaging (1 paper), AI in cancer detection (1 paper) and Lung Cancer Diagnosis and Treatment (1 paper). Sinan Onogur is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (1 paper), AI in cancer detection (1 paper) and Lung Cancer Diagnosis and Treatment (1 paper). Sinan Onogur collaborates with scholars based in Germany, Austria and United States. Sinan Onogur's co-authors include Keno März, Alexander Seitel, Lena Maier‐Hein, Doğu Teber, Tal Arbel, Bram van Ginneken, Michal Kozubek, Julio Sáez-Rodríguez, Allan Hanbury and Annette Kopp‐Schneider and has published in prestigious journals such as Der Urologe and Zenodo (CERN European Organization for Nuclear Research).

In The Last Decade

Sinan Onogur

2 papers receiving 9 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Sinan Onogur Germany	2	7	3	2	2	2	2	9
S. H. Feng China	2	7 1.0×	2 0.7×	1 0.5×			5	12
Mohyee Ayouty United States	2	4 0.6×	3 1.0×				5	5
E Westwood United Kingdom	2	6 0.9×	2 0.7×				3	7
Helmut Diers Germany	2	7 1.0×	2 0.7×				5	8
Cleide Barrigoto Portugal	2	4 0.6×	4 1.3×				3	5
Rita Fernandez Garda United Kingdom	2	4 0.6×	5 1.7×				3	6
H. Taright France	2	5 0.7×	2 0.7×				2	7
Tomohagu Sugie Japan	2	5 0.7×	2 0.7×				2	8
Mara Franza Italy	3	3 0.4×	3 1.0×				8	12
Mislav Nedić Croatia	1	6 0.9×	3 1.0×				2	7

Countries citing papers authored by Sinan Onogur

Since Specialization

Citations

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

Fields of papers citing papers by Sinan Onogur

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sinan Onogur

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

All Works

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2 of 2 papers shown

1.

Teber, Doğu, et al.. (2020). Wie weit ist Chirugie 4.0?. Der Urologe. 59(9). 1035–1043. 8 indexed citations

2.

Maier‐Hein, Lena, Annika Reinke, Michal Kozubek, et al.. (2020). Biomedical Image Analysis Challenges (BIAS) Reporting Guideline. Zenodo (CERN European Organization for Nuclear Research). 1 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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