Devrim Ünay

2.2k total citations
80 papers, 880 citations indexed

About

Devrim Ünay is a scholar working on Computer Vision and Pattern Recognition, Biophysics and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Devrim Ünay has authored 80 papers receiving a total of 880 indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Computer Vision and Pattern Recognition, 15 papers in Biophysics and 13 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Devrim Ünay's work include Medical Image Segmentation Techniques (16 papers), Image Retrieval and Classification Techniques (16 papers) and Cell Image Analysis Techniques (14 papers). Devrim Ünay is often cited by papers focused on Medical Image Segmentation Techniques (16 papers), Image Retrieval and Classification Techniques (16 papers) and Cell Image Analysis Techniques (14 papers). Devrim Ünay collaborates with scholars based in Türkiye, Netherlands and United States. Devrim Ünay's co-authors include Bernard Gosselin, Ahmet Ekin, Radu Jasinschi, Müjdat Çetin, Olivier Debeir, Henning Müller, Marie-France Destain, Olivier Kleynen, Vincent Leemans and Tolga Taşdizen and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and Neuroscience.

In The Last Decade

Devrim Ünay

75 papers receiving 801 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Devrim Ünay Türkiye 17 284 263 254 118 103 80 880
Son Vu Truong Dao Vietnam 16 74 0.3× 95 0.4× 205 0.8× 109 0.9× 58 0.6× 54 762
Mostafa Mehdipour Ghazi Denmark 8 165 0.6× 124 0.5× 265 1.0× 142 1.2× 34 0.3× 19 674
J. Satheesh Kumar India 13 100 0.4× 95 0.4× 117 0.5× 64 0.5× 73 0.7× 28 664
Jagadeesh Pujari India 14 311 1.1× 210 0.8× 348 1.4× 65 0.6× 24 0.2× 53 753
Tommy Löfstedt Sweden 14 103 0.4× 89 0.3× 50 0.2× 144 1.2× 78 0.8× 35 754
Enrique Fernández-Blanco Spain 17 298 1.0× 95 0.4× 37 0.1× 186 1.6× 123 1.2× 40 912
Miao Li China 11 186 0.7× 60 0.2× 39 0.2× 107 0.9× 48 0.5× 58 464
Woo Chaw Seng Malaysia 13 379 1.3× 83 0.3× 100 0.4× 151 1.3× 70 0.7× 39 776
M. Cevdet İnce Türkiye 9 270 1.0× 50 0.2× 59 0.2× 392 3.3× 138 1.3× 25 808
Md Jahid Hasan Bangladesh 11 45 0.2× 144 0.5× 280 1.1× 83 0.7× 27 0.3× 32 534

Countries citing papers authored by Devrim Ünay

Since Specialization
Citations

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

Fields of papers citing papers by Devrim Ünay

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Devrim Ünay

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

All Works

20 of 20 papers shown
1.
Iheme, Leonardo O., et al.. (2024). Novel Neural Style Transfer based data synthesis method for phase-contrast wound healing assay images. Biomedical Signal Processing and Control. 96. 106514–106514. 1 indexed citations
2.
Iheme, Leonardo O., Sevgi Önal, Özden Yalcin-Ozuysal, et al.. (2024). Collection: Wound Healing Assay Dataset (WHAD) and Cell Adhesion and Motility Assay Dataset (CAMAD). Istanbul Technical University Academic Open Archive (Istanbul Technical University). 1. 95–102. 1 indexed citations
3.
Kollias, Dimitrios, et al.. (2024). COVID-19 Detection from Computed Tomography Images Using Slice Processing Techniques and a Modified Xception Classifier. International Journal of Biomedical Imaging. 2024. 1–9. 2 indexed citations
4.
Yao, Junlie, Jie Xing, Zheng Fang, et al.. (2023). Dual-infinite coordination polymer-engineered nanomedicines for dual-ion interference-mediated oxidative stress-dependent tumor suppression. Materials Horizons. 10(6). 2109–2119. 6 indexed citations
5.
Ünay, Devrim, et al.. (2023). Covid-19 detection using modified xception transfer learning approach from computed tomography images. International Journal of Advances in Intelligent Informatics. 9(3). 524–524. 3 indexed citations
6.
Erdil, Ertunç, et al.. (2022). An interactive time series image analysis software for dendritic spines. Scientific Reports. 12(1). 12405–12405. 12 indexed citations
7.
Yalcin-Ozuysal, Özden, et al.. (2021). An Image Segmentation Method for Wound Healing Assay Images. Istanbul Technical University Academic Open Archive (Istanbul Technical University). 4(1). 30–37. 1 indexed citations
8.
Yalcin-Ozuysal, Özden, et al.. (2021). Improved cell segmentation using deep learning in label-free optical microscopy images. TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES. 29(SI-1). 2855–2868. 3 indexed citations
9.
Ünay, Devrim, et al.. (2021). Model-free automatic segmentation of the aortic valve in multislice computed tomography images. SHILAP Revista de lepidopterología. 27(2). 122–128.
10.
Paavolainen, Lassi, Volker Bäcker, Benjamin Pavie, et al.. (2020). BIAFLOWS: A Collaborative Framework to Reproducibly Deploy and Benchmark Bioimage Analysis Workflows. Patterns. 1(3). 100040–100040. 21 indexed citations
11.
Özkan, Çiğdem, Kaya Oğuz, Hasan Demirel, et al.. (2019). Diagnosis of Bipolar Disease Using Correlation-Based Feature Selection with Different Classification Methods. 1–4. 2 indexed citations
12.
Ünay, Devrim, et al.. (2018). Modified Visual Magnetic Resonance Rating Scale for Evaluation of Patients with Forgetfulness. Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques. 46(1). 71–78. 4 indexed citations
13.
Rada, Lavdie, et al.. (2018). Tracking-assisted Detection of Dendritic Spines in Time-Lapse Microscopic Images. Neuroscience. 394. 189–205. 6 indexed citations
14.
Israely, Inbal, et al.. (2016). Dendritic spine classification using shape and appearance features based on two-photon microscopy. Journal of Neuroscience Methods. 279. 13–21. 31 indexed citations
15.
Ünay, Devrim, et al.. (2013). Region growing on frangi vesselness values in 3-D CTA data. 12. 1–4. 1 indexed citations
16.
Ünay, Devrim, Cengizhan Öztürk, & Maureen Stone. (2012). Single syllable tongue motion analysis using tagged cine MRI. Computer Methods in Biomechanics & Biomedical Engineering. 17(8). 853–864. 2 indexed citations
17.
Ünay, Devrim, Ahmet Ekin, & Radu Jasinschi. (2010). Local Structure-Based Region-of-Interest Retrieval in Brain MR Images. IEEE Transactions on Information Technology in Biomedicine. 14(4). 897–903. 67 indexed citations
18.
Ünay, Devrim, et al.. (2009). Medical Image Retrieval and Automatic Annotation: VPA-SABANCI at ImageCLEF 2009. Sabanci University. 5 indexed citations
19.
Ünay, Devrim & Bernard Gosselin. (2008). Apple Defect Segmentation by Artificial Neural Networks. ORBi UMONS. 3 indexed citations
20.
Ünay, Devrim & Bernard Gosselin. (2004). A Quality Grading Approach for Jonagold Apples. ORBi UMONS. 3 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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