Can Koyuncu

681 total citations
21 papers, 253 citations indexed

About

Can Koyuncu is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Can Koyuncu has authored 21 papers receiving a total of 253 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Computer Vision and Pattern Recognition, 10 papers in Artificial Intelligence and 7 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Can Koyuncu's work include AI in cancer detection (10 papers), Radiomics and Machine Learning in Medical Imaging (7 papers) and Cell Image Analysis Techniques (7 papers). Can Koyuncu is often cited by papers focused on AI in cancer detection (10 papers), Radiomics and Machine Learning in Medical Imaging (7 papers) and Cell Image Analysis Techniques (7 papers). Can Koyuncu collaborates with scholars based in United States, Türkiye and Switzerland. Can Koyuncu's co-authors include Çiğdem Gündüz-Demir, Rengül Çetin-Atalay, Anant Madabhushi, Cheng Lu, Andrew Janowczyk, Xiangxue Wang, Salim Arslan, İrem Durmaz, Vamsidhar Velcheti and Kaustav Bera and has published in prestigious journals such as Journal of Clinical Oncology, PLoS ONE and Science Advances.

In The Last Decade

Can Koyuncu

18 papers receiving 248 citations

Peers

Can Koyuncu

RNMI

BIOPH

CVPR

ONCOL

Eva Bozsaky Austria

Albert Xthona Belgium

David Romo‐Bucheli Colombia

Wei-Hsiang Yu Taiwan

Rob van de Loo Netherlands

Trissia Brown United States

Mart van Rijthoven Netherlands

Christine England United States

M. Milagro Fernández-Carrobles Spain

Lorraine Corsale United States

Eva Bozsaky Austria

Can Koyuncu

44 ×0.4

48 ×0.5

RNMI

49 ×0.7

BIOPH

37 ×0.5

CVPR

38 ×0.6

ONCOL

Citations per year, relative to Can Koyuncu Can Koyuncu (= 1×) peers Eva Bozsaky

Countries citing papers authored by Can Koyuncu

Since Specialization

Citations

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

Fields of papers citing papers by Can Koyuncu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Can Koyuncu

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

All Works

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20 of 20 papers shown

Corredor, Germán, Jonathan Harris, Can Koyuncu, et al.. (2024). Metrics Derived from Architecture of Tumor-Infiltrating Lymphocytes are Associated with Overall Survival in HPV-Positive Oropharyngeal Squamous Cell Carcinoma Patients: Results from NRG/RTOG 0129 and 0522. International Journal of Radiation Oncology*Biology*Physics. 120(2). S130–S130. 1 indexed citations

Koyuncu, Can, et al.. (2024). A Guided-Ensembling Approach for Cell Counting in Fluorescence Microscopy Images. IEEE Access. 12. 195552–195560.

Koyuncu, Can, Andrew Janowczyk, Xavier Farré, et al.. (2023). Visual Assessment of 2-Dimensional Levels Within 3-Dimensional Pathology Data Sets of Prostate Needle Biopsies Reveals Substantial Spatial Heterogeneity. Laboratory Investigation. 103(12). 100265–100265. 3 indexed citations

Koyuncu, Can, Mitchell J. Frederick, Lester D.�R. Thompson, et al.. (2023). Machine learning driven index of tumor multinucleation correlates with survival and suppressed anti-tumor immunity in head and neck squamous cell carcinoma patients. Oral Oncology. 143. 106459–106459. 7 indexed citations

Serafin, Robert, Can Koyuncu, Weisi Xie, et al.. (2023). Nondestructive 3D pathology with analysis of nuclear features for prostate cancer risk assessment. The Journal of Pathology. 260(4). 390–401. 13 indexed citations

Wang, Zhao, et al.. (2023). Measuring dense false positive regions from segmentation result for whole slide tissue histology image. Journal of Visual Communication and Image Representation. 96. 103929–103929. 1 indexed citations

Castro, Patricia, Germán Corredor, Can Koyuncu, et al.. (2023). Recurrent Oropharyngeal Squamous Cell Carcinomas Maintain Anti-tumor Immunity and Multinucleation Levels Following Completion of Radiation. Head and Neck Pathology. 17(4). 952–960.

Koyuncu, Can, et al.. (2023). Transformer as a spatially-aware multi-instance learning framework to predict the risk of death for early-stage non-small cell lung cancer. 33–33. 1 indexed citations

Koyuncu, Can, Paula Toro, Germán Corredor, et al.. (2022). A machine learning model for separating epithelial and stromal regions in oral cavity squamous cell carcinomas using H&E-stained histology images: A multi-center, retrospective study. Oral Oncology. 131. 105942–105942. 12 indexed citations

10.

Koyuncu, Can, Cheng Lu, Rainer Grobholz, et al.. (2022). Multi-site cross-organ calibrated deep learning (MuSClD): Automated diagnosis of non-melanoma skin cancer. Medical Image Analysis. 84. 102702–102702. 15 indexed citations

11.

Wang, Xiangxue, Cristian Barrera, Kaustav Bera, et al.. (2022). Spatial interplay patterns of cancer nuclei and tumor-infiltrating lymphocytes (TILs) predict clinical benefit for immune checkpoint inhibitors. Science Advances. 8(22). eabn3966–eabn3966. 48 indexed citations

12.

Serafin, Robert, Weisi Xie, Can Koyuncu, & Jonathan Liu. (2022). Non-destructive 3D pathology with analysis of nuclear features for prostate cancer risk assessment. TM2B.3–TM2B.3. 1 indexed citations

13.

Yang, Kailin, Cheng Lu, Lin Li, et al.. (2021). Radiomic Features Associated With HPV Status on Pretreatment Computed Tomography in Oropharyngeal Squamous Cell Carcinoma Inform Clinical Prognosis. Frontiers in Oncology. 11. 744250–744250. 22 indexed citations

14.

Lu, Cheng, Can Koyuncu, Germán Corredor, et al.. (2020). Feature-driven local cell graph (FLocK): New computational pathology-based descriptors for prognosis of lung cancer and HPV status of oropharyngeal cancers. Medical Image Analysis. 68. 101903–101903. 41 indexed citations

15.

Koyuncu, Can, Andrew Janowczyk, Cheng Lu, et al.. (2020). Three-dimensional histo-morphometric features from light sheet microscopy images result in improved discrimination of benign from malignant glands in prostate cancer. 15–15. 2 indexed citations

16.

Koyuncu, Can, Rengül Çetin-Atalay, & Çiğdem Gündüz-Demir. (2018). Object‐Oriented Segmentation of Cell Nuclei in Fluorescence Microscopy Images. Cytometry Part A. 93(10). 1019–1028. 14 indexed citations

17.

Koyuncu, Can, et al.. (2016). Iterative h‐minima‐based marker‐controlled watershed for cell nucleus segmentation. Cytometry Part A. 89(4). 338–349. 21 indexed citations

18.

Koyuncu, Can, et al.. (2014). Two-Tier Tissue Decomposition for Histopathological Image Representation and Classification. IEEE Transactions on Medical Imaging. 34(1). 275–283. 16 indexed citations

19.

Koyuncu, Can, İrem Durmaz, Rengül Çetin-Atalay, & Çiğdem Gündüz-Demir. (2014). A supervised learning model for live cell segmentation. Bilkent University Institutional Repository (Bilkent University). 1971–1974.

20.

Koyuncu, Can, Salim Arslan, İrem Durmaz, Rengül Çetin-Atalay, & Çiğdem Gündüz-Demir. (2012). Smart Markers for Watershed-Based Cell Segmentation. PLoS ONE. 7(11). e48664–e48664. 33 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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