Gözde Ünal

11.4k citations
120 papers · 2.4k indexed · h-index 26

Gözde Ünal

116 papers receiving 2.3k citations

Peers

Gözde Ünal
Comparison fields: 5 of 164
  • Neurology 580
  • Computer Vision and Pattern Recognition 1.0k
  • Health Informatics 34
  • Media Technology 217
  • Radiology, Nuclear Medicine and Imaging 477
Replace Qingmao Hu with:
Qingmao Hu China
Tal Arbel Canada
Shihui Ying China
Heinz Handels Germany
Aaron Carass United States
Fucang Jia China
Jian Yang China
Xavier Lladó Spain
Abdel Aziz Taha Germany
Ender Konukoğlu Switzerland
Gözde Ünal relative to Qingmao Hu China Qingmao Hu's profile →
Citations per field
00.5×1.6×
Qingmao Hu · 1×
Citations per year

Countries citing papers authored by Gözde Ünal

Since Specialization
Citations

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

Fields of papers citing papers by Gözde Ünal

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

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

All Works

20 of 20 papers shown
#Work
1 20251
2 20244
3 20241
4 20241
5 20231
6 20235
7 202123
8 202081
9 202017
10 2019102
11 201954
12 2018105
13 201879
14 201677
15 201318
16 20121
17 20091
18 200933
19 2008101
20 200716

About Gözde Ünal

Gözde Ünal is a scholar working on Computational Mathematics, Computer Vision and Pattern Recognition, Neurology, Space and Planetary Science and Computer Graphics and Computer-Aided Design, having authored 120 papers that have together received 2.4k indexed citations. Recurring topics across this work include Medical Image Segmentation Techniques (35 papers), Transcranial Magnetic Stimulation Studies (16 papers), Image Retrieval and Classification Techniques (10 papers), 3D Shape Modeling and Analysis (10 papers), Image and Object Detection Techniques (9 papers), Advanced Neural Network Applications (9 papers), Image and Signal Denoising Methods (8 papers) and Coronary Interventions and Diagnostics (8 papers). The work is most often cited by research in Neurology (580 citations), Computer Vision and Pattern Recognition (1.0k citations), Health Informatics (34 citations), Media Technology (217 citations) and Radiology, Nuclear Medicine and Imaging (477 citations). Gözde Ünal has collaborated with scholars based in Türkiye, United States and Germany. Frequent co-authors include Greg Slabaugh, Tong Fang, Anthony Yezzi, Andaç Hamamcı, N. Kucuk, K. Engin, Marom Bikson, Hamid Krim, Kutlay Karaman and Stéphane Carlier. Their work appears in journals such as Brain stimulation, IEEE Transactions on Image Processing, Pattern Recognition, IEEE Transactions on Medical Imaging and Scientific Reports.

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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