Ertunç Erdil

1.0k total citations · 1 hit paper
23 papers, 396 citations indexed

About

Ertunç Erdil is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Media Technology. According to data from OpenAlex, Ertunç Erdil has authored 23 papers receiving a total of 396 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Computer Vision and Pattern Recognition, 8 papers in Artificial Intelligence and 6 papers in Media Technology. Recurrent topics in Ertunç Erdil's work include Medical Image Segmentation Techniques (10 papers), Image Retrieval and Classification Techniques (6 papers) and Image Processing Techniques and Applications (6 papers). Ertunç Erdil is often cited by papers focused on Medical Image Segmentation Techniques (10 papers), Image Retrieval and Classification Techniques (6 papers) and Image Processing Techniques and Applications (6 papers). Ertunç Erdil collaborates with scholars based in Türkiye, United States and Portugal. Ertunç Erdil's co-authors include Ender Konukoğlu, Krishna Chaitanya, Neerav Karani, Müjdat Çetin, Tolga Taşdizen, Devrim Ünay, Lavdie Rada, Inbal Israely, Bruno Weber and Theofanis Karayannis and has published in prestigious journals such as Nature Communications, Bioinformatics and Scientific Reports.

In The Last Decade

Ertunç Erdil

23 papers receiving 384 citations

Hit Papers

Local contrastive loss with pseudo-label based self-train... 2023 2026 2024 2025 2023 25 50 75 100
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation
    2023 112 citations Krishna Chaitanya, Ertunç Erdil et al. Medical Image Analysis profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ertunç Erdil Türkiye 9 225 195 104 44 39 23 396
Ender Konukoglu Switzerland 7 194 0.9× 159 0.8× 182 1.8× 38 0.9× 51 1.3× 12 494
Hatice Çınar Akakın Türkiye 7 241 1.1× 108 0.6× 56 0.5× 26 0.6× 15 0.4× 11 416
Jiazhou Chen China 14 142 0.6× 197 1.0× 124 1.2× 87 2.0× 11 0.3× 46 610
Mahdieh Soleymani Baghshah Iran 15 323 1.4× 423 2.2× 59 0.6× 33 0.8× 21 0.5× 52 676
Firdaus Janoos United States 12 99 0.4× 110 0.6× 64 0.6× 28 0.6× 11 0.3× 32 351
Neelam Sinha India 11 162 0.7× 87 0.4× 157 1.5× 33 0.8× 23 0.6× 73 404
Xiaoxiao Liu China 8 181 0.8× 69 0.4× 118 1.1× 71 1.6× 13 0.3× 18 449
Mohamed Ben Slima Tunisia 11 262 1.2× 113 0.6× 126 1.2× 45 1.0× 260 6.7× 48 490
Jundong Liu United States 9 196 0.9× 95 0.5× 49 0.5× 43 1.0× 64 1.6× 35 362
Ahmed Fakhry United States 10 153 0.7× 106 0.5× 82 0.8× 21 0.5× 31 0.8× 18 408

Countries citing papers authored by Ertunç Erdil

Since Specialization
Citations

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

Fields of papers citing papers by Ertunç Erdil

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ertunç Erdil

This figure shows the co-authorship network connecting the top 25 collaborators of Ertunç Erdil. A scholar is included among the top collaborators of Ertunç Erdil 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 Ertunç Erdil. Ertunç Erdil 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.
Erdil, Ertunç, Anton S. Becker, Moritz Schwyzer, et al.. (2024). Predicting standardized uptake value of brown adipose tissue from CT scans using convolutional neural networks. Nature Communications. 15(1). 8402–8402. 1 indexed citations
2.
Erdil, Ertunç, et al.. (2023). Wiener Guided DIP for Unsupervised Blind Image Deconvolution. 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). 3046–3055. 3 indexed citations
3.
Chaitanya, Krishna, Ertunç Erdil, Neerav Karani, & Ender Konukoğlu. (2023). Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation. Medical Image Analysis. 87. 102792–102792. 112 indexed citations breakdown →
4.
Erdil, Ertunç, et al.. (2022). An interactive time series image analysis software for dendritic spines. Scientific Reports. 12(1). 12405–12405. 12 indexed citations
5.
Karani, Neerav, Ertunç Erdil, Krishna Chaitanya, & Ender Konukoğlu. (2020). Test-time adaptable neural networks for robust medical image segmentation. Medical Image Analysis. 68. 101907–101907. 103 indexed citations
6.
Erdil, Ertunç, Krishna Chaitanya, & Ender Konukoğlu. (2020). Unsupervised out-of-distribution detection using kernel density estimation. 3 indexed citations
7.
Erdil, Ertunç, et al.. (2019). Pseudo-Marginal MCMC Sampling for Image Segmentation Using Nonparametric Shape Priors. IEEE Transactions on Image Processing. 28(11). 5702–5715. 4 indexed citations
8.
Rada, Lavdie, et al.. (2018). Tracking-assisted Detection of Dendritic Spines in Time-Lapse Microscopic Images. Neuroscience. 394. 189–205. 6 indexed citations
9.
Erdil, Ertunç, et al.. (2017). Nonparametric Joint Shape and Feature Priors for Image Segmentation. IEEE Transactions on Image Processing. 26(11). 5312–5323. 13 indexed citations
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Erdil, Ertunç, et al.. (2017). Disjunctive Normal Shape Boltzmann Machine. 2357–2361. 5 indexed citations
12.
Erdil, Ertunç, et al.. (2017). Image Segmentation Using Disjunctive Normal Bayesian Shape and Appearance Models. IEEE Transactions on Medical Imaging. 37(1). 293–305. 9 indexed citations
13.
Erdil, Ertunç, et al.. (2017). A Markov Chain Monte Carlo based rigid image registration method. 77. 1–4. 1 indexed citations
14.
Erdil, Ertunç, et al.. (2016). MCMC Shape Sampling for Image Segmentation with Nonparametric Shape Priors. 411–419. 9 indexed citations
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Erdil, Ertunç, et al.. (2013). An efficient and scalable family of algorithms for combining clusterings. Engineering Applications of Artificial Intelligence. 26(10). 2525–2539. 15 indexed citations
18.
Erdil, Ertunç, et al.. (2011). ASOD: Arbitrary shape object detection. Engineering Applications of Artificial Intelligence. 24(7). 1295–1299. 7 indexed citations
19.
Erdil, Ertunç, et al.. (2010). Obtaining better quality final clustering by merging a collection of clusterings. Bioinformatics. 26(20). 2645–2646. 6 indexed citations
20.
Erdil, Ertunç, et al.. (2010). Combining multiple clusterings using similarity graph. Pattern Recognition. 44(3). 694–703. 63 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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