Publications

Journals

  1. M.B. Erden, S. Unver, I.A. Gurses, R. Turkay, C. Gunduz-Demir, “PI-Att: Topology attention for segmentation networks through adaptive persistence image representation,” https://arxiv.org/abs/2408.08038.

  2. M.B. Erden, S. Cansiz, O. Caki, H. Khattak, D. Etiz, M.C. Yakar, K. Duruer, B. Barut, and C. Gunduz-Demir, “FourierLoss: Shape-aware loss function with Fourier descriptors,” https://arxiv.org/abs/2309.12106.

  3. S. Ozcelik, S. Unver, I.A. Gurses, R. Turkay, and C. Gunduz-Demir, “Topology-aware loss for aorta and great vessel segmentation in computed tomography images,” https://arxiv.org/abs/2307.03137.

  4. B. Bolat, O.C. Eren, E.H. Dur-Karasayar, C. Aydin Mericoz, C. Gunduz-Demir, I. Kulac, “Large language models as a rapid and objective tool for pathology report data extraction,” Turkish Journal of Pathology, 40(2):138-141, 2024.

  5. R. Demir, S. Koc, D.G. Ozturk, S. Bilir, H.I. Ozata, R. Williams, J. Christy, Y. Akkoc, I. Tinay, C. Gunduz-Demir, D. Gozuacik, “Artificial intelligence assisted patient blood and urine droplet pattern analysis for non-invasive and accurate diagnosis of bladder cancer,” Scientific Reports, 14:2488, 2024.

  6. M. Firouznia, J.A. Koupaei, K. Faez, A.S. Jabdaragh, and C. Gunduz-Demir, “FractalRG: Advanced fractal region growing using Gaussian mixture models for left atrium segmentation,” Digital Signal Processing, 147:104411, 2024.

  7. T.B. Kanmaz, E. Ozturk, H.V. Demir, and C. Gunduz-Demir, “Deep learning-enabled electromagnetic near-field prediction and inverse design of metasurfaces,” Optica, 10(10):1373-1382, 2023.

  8. S. Cansiz, C. Kesim, S.N. Bektas, Z. Kulali, M. Hasanreisoglu, and C. Gunduz-Demir, “FourierNet: Shape-preserving network for Henle’s fiber layer segmentation in optical coherence tomography images,” IEEE Journal of Biomedical and Health Informatics, 27(2):1036-1047, 2023.

  9. A.S. Jabdaragh, M. Firouznia, K. Faez, F. Alikhani, J.A. Koupaei, and C. Gunduz-Demir, “MTFD-Net: Left atrium segmentation using multi-task network in CT images through fractal dimension estimation,” Pattern Recognition Letters, 173:108-114, 2023.

  10. C. Kesim, S.N. Bektas, Z. Kulali, E. Yildiz, M.G. Ersoz, A. Sahin, C. Gunduz-Demir, and M. Hasanreisoglu, “Henle fiber layer mapping with directional optical coherence tomography,” RETINA The Journal of Retinal and Vitreous Diseases, 42(9):1780-1787, 2022.

  11. G.N. Gunesli, C. Sokmensuer, and C. Gunduz-Demir, “AttentionBoost: Learning what to attend for gland segmentation in histopathological images by boosting fully convolutional networks,” IEEE Transactions on Medical Imaging, 39(12):4262-4273, 2020. [pdf]

  12. C.F. Koyuncu, G.N. Gunesli, R. Cetin-Atalay, and C. Gunduz-Demir, “DeepDistance: A multi-task deep regression model for cell detection in inverted microscopy images,” Medical Image Analysis, 63:101720, 2020. [pdf]

  13. C.T. Sari and C. Gunduz-Demir, “Unsupervised feature extraction via deep learning for histopathological classification of colon tissue images, IEEE Transactions on Medical Imaging, 38(5):1139-1149, 2019. [pdf]

  14. C.F. Koyuncu, R. Cetin-Atalay, and C. Gunduz-Demir, “Object-oriented segmentation of cell nuclei in fluorescence microscopy images,” Cytometry: Part A, 93A(10):1019-1028, 2018. [pdf] [Highlighted on the front cover]

  15. B. Berg-Johansen, M. Han, A.J. Fields, E.C. Liebenberg, B.J. Lim, P.E.Z. Larson, C. Gunduz-Demir, G.J. Kazakia, R.Krug, J.C. Lotz, “Cartilage endplate thickness variation measured by ultrashort echo-time MRI is associated with adjacent disc degeneration,” Spine, 43(10):E592-E600, 2018. [pdf]

  16. C.F. Koyuncu, A. Akhan, T. Ersahin, R. Cetin-Atalay, and C. Gunduz-Demir, “Iterative h-minima based marker-controlled watershed for cell nucleus segmentation,” Cytometry: Part A, 89A:338-249, 2016. [pdf]

  17. T. Gultekin, C.F. Koyuncu, C. Sokmensuer, and C. Gunduz-Demir, “Two-tier tissue decomposition for histopathological image representation and classification,” IEEE Transactions on Medical Imaging, 34(1):275-283, 2015. [pdf]

  18. S. Arslan, E. Ozyurek, and C. Gunduz-Demir, “A color and shape based algorithm for segmentation of white blood cells in peripheral blood and bone marrow images,” Cytometry: Part A, 85(6):480-490, 2014. [pdf]

  19. G. Olgun, C. Sokmensuer, and C. Gunduz-Demir, “Local object patterns for tissue image representation and cancer classification,” IEEE Journal of Biomedical and Health Informatics, 18(4):1390-1396, 2014. [pdf]

  20. S. Arslan, T. Ersahin, R. Cetin-Atalay, and C. Gunduz-Demir, “Attributed relational graphs for cell nucleus segmentation in fluorescence microscopy images,” IEEE Transactions on Medical Imaging, 32(6):1121-1131, 2013. [pdf]

  21. E. Ozdemir and C. Gunduz-Demir, “A hybrid classification model for digital pathology using structural and statistical pattern recognition,” IEEE Transactions on Medical Imaging, 32(2):474-483, 2013. [pdf]

  22. R. Ali, C. Gunduz-Demir, T.Szilagyi, B. Durkee, and E.E. Graves, “Semi automatic segmentation of subcutaneous tumors from micro-computed tomography images,” Physics in Medicine and Biology, 58:8007-8019, 2013. [pdf]

  23. C.F. Koyuncu, S. Arslan, I. Durmaz, R. Cetin-Atalay, and C. Gunduz-Demir, “Smart markers for watershed-based cell segmentation,” PLoS ONE, 7(11):e48664, 2012. [pdf]

  24. E. Ozdemir, C. Sokmensuer, and C. Gunduz-Demir, “A resampling-based Markovian model for automated colon cancer diagnosis,” IEEE Transactions on Biomedical Engineering, 59(1):281-289, 2012. [pdf]

  25. A.C. Simsek, A.B. Tosun, C. Aykanat, C. Sokmensuer, and C. Gunduz-Demir, “Multilevel segmentation of histopathological images using cooccurrence of tissue objects,” IEEE Transactions on Biomedical Engineering, 59(6):1681-1690, 2012. [pdf]

  26. A.B. Tosun and C. Gunduz-Demir, “Graph run-length matrices for histopathological image segmentation,” IEEE Transactions on Medical Imaging, 30(3):721-732, 2011. [pdf]

  27. C. Gunduz-Demir, M. Kandemir, A.B. Tosun, and C. Sokmensuer, “Automatic segmentation of colon glands using object-graphs,” Medical Image Analysis, 14(1):1-12, 2010. [pdf]

  28. D. Altunbay, C. Cigir, C. Sokmensuer, and C. Gunduz-Demir, “Color graphs for automated cancer diagnosis and grading,” IEEE Transactions on Biomedical Engineering, 57(3):665-674, 2010. [pdf]

  29. M. Cebe and C. Gunduz-Demir, “Qualitative test-cost sensitive classification,” Pattern Recognition Letters, 31(13):2043-2051, 2010. [pdf]

  30. A.B. Tosun, M. Kandemir, C. Sokmensuer, and C. Gunduz-Demir, “Object-oriented texture analysis for the unsupervised segmentation of biopsy images for cancer detection,” Pattern Recognition, 42(6):1104-1112, 2009. [pdf]

  31. C. Gunduz-Demir, “Mathematical modeling of the malignancy of cancer using graph evolution,” Mathematical Biosciences, 209(2):514-527, 2007. [pdf]

  32. C. Demir, S.H. Gultekin, and B. Yener, “Learning the topological properties of brain tumors,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2(3):262-270, 2005.

  33. C. Demir, S.H. Gultekin, and B. Yener, “Augmented cell-graphs for automated cancer diagnosis,” Bioinformatics, 21(Suppl 2):ii7-ii12, 2005.

  34. C. Demir and E. Alpaydin, “Cost-conscious classifier ensembles,” Pattern Recognition Letters, 26(14):2206-2214, 2005.

  35. C. Gunduz, B. Yener, and S.H. Gultekin, “The cell-graphs of cancer,” Bioinformatics, 20(Suppl 1):i145-i151, 2004.

Conferences

  1. G. Olgun, C. Sokmensuer, and C. Gunduz-Demir, “Graph walks for classification of histopathological images,” International Symposium on Biomedical Imaging: From Nano to Macro, San Francisco, CA, Apr 2013.

  2. C. Gunduz-Demir, “Tissue object patterns for segmentation in histopathological images,” 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies, Barcelona, Spain, Oct 2011.

  3. A.B. Tosun, C. Sokmensuer, and C. Gunduz-Demir, “Unsupervised tissue image segmentation through object-oriented texture,” 20th International Conference on Pattern Recognition, Istanbul, Turkey, Aug 2010.

  4. M. Cebe and C. Gunduz-Demir, “Test-cost sensitive classification based on conditioned loss functions,” 18th European Conference on Machine Learning, Warsaw, Poland, Sep 2007 (published in LNAI 4701, Springer-Verlag).

  5. C.C.Bilgin, C. Demir, C. Nagi, and B. Yener, “Cell-graph mining for breast tissue modeling and classification,” 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Lyon, France, Aug 2007.

  6. C. Demir, S.H. Gultekin, and B. Yener, “Augmented cell-graphs for automated cancer diagnosis,” 4th European Conference on Computational Biology, Madrid, Spain, Sep 2005.

  7. C. Demir, S.H. Gultekin, and B. Yener, “Spectral analysis of cell-graphs for automated cancer diagnosis,” 4th Conference on Modeling and Simulation in Biology, Medicine and Biomedical Engineering, Linkoping, Sweden, May 2005.

  8. C. Demir, S.H. Gultekin, and B. Yener, “Rule-based automated cancer diagnosis,” 2nd Annual Rocky Mountain Regional Bioinformatics Conference, Aspen, Colorado, Dec 2004.

  9. C. Gunduz, B. Yener, and S.H. Gultekin, “The cell-graphs of cancer,” 12th International Conference on Intelligent Systems for Molecular Biology (ISMB), Glasgow, Scotland, Aug 2004.

  10. C. Gunduz, O.T. Yildiz, E. Alpaydin, and T. Bilgic, “An algorithm to learn the structure of a Bayesian network,” 10th Turkish Symposium on Artificial Intelligence and Neural Networks, Jun 2001.

National Conferences

  1. S. Arslan, I. Durmaz, R. Cetin-Atalay, and C. Gunduz-Demir, “Unsupervised segmentation of live cell images using Gaussian modeling,” IEEE 19th Signal Processing, Communication, and Applications Conference, Antalya, Turkey, Apr 2011.

  2. E.B. Ozgul, C. Sokmensuer, and C. Gunduz-Demir, “Detection of colon glands using subgraph modeling,” IEEE 19th Signal Processing, Communication, and Applications Conference, Antalya, Turkey, Apr 2011.

  3. E. Ozdemir, C. Sokmensuer, and C. Gunduz-Demir, “Histopathological image classification with the bag of words model,” IEEE 19th Signal Processing, Communication, and Applications Conference, Antalya, Turkey, Apr 2011.

  4. C. Cigir, C. Sokmensuer, and C. Gunduz-Demir, “Mathematical analysis of colon glands for cancer diagnosis,” IEEE 17th Signal Processing, Communication, and Applications Conference, Antalya, Turkey, Apr 2009.

Invited Talks

  1. C. Gunduz-Demir, “Tıp ve yapay zeka: Medikal goruntulemenin geleceginde ogrenen bilgisayarlar (Medicine and artificial intelligence: Computers that can learn in the future of medical imaging),” 7th Multidisciplinary Head and Neck Cancer Congress, Antalya, Turkey, Feb 2022.

  2. C. Gunduz-Demir, “Segmentation networks for digital pathology,” University of Warwick, Coventry, UK, Dec 2021.

  3. C. Gunduz-Demir, “How is AI shaping the future of medical imaging?,” 4th İstanbul Privacy Symposium, Istanbul, Turkey, Dec 2021.

  4. C. Gunduz-Demir, “Patolojik goruntulerin makine ogrenmesine dayali analizi (Pathological image analysis based on machine learning),” 29th National Pathology Congress, Trabzon, Turkey, Oct 2019.

  5. C. Gunduz-Demir, “Deep learning for medical image analysis,” A*Star Bioinformatics Institute, Singapore, Aug 2019.

  6. C. Gunduz-Demir, “Medikal goruntulemede veri analizi (Data analysis in medical imaging),” Interdisciplinary Brainstorming Meeting on Big Data, Hacettepe University, Ankara, Turkey, Mar 2019.

  7. C. Gunduz-Demir, “Object-based modeling of medical images,” Qatar Computing Research Institute, Qatar Foundation, Doha, Qatar, Jan 2017.

  8. C. Gunduz-Demir, “Object-based modeling of medical images,” Fraunhofer Institute for Integrated Circuits IIS, Collaborative Research Center, Erlangen, Germany, Oct 2016.

  9. C. Gunduz-Demir, “Model-based algorithms for medical image analysis,” Middle East Technical University, Department of Biomedical Engineering, Ankara, Turkey, Nov 2014.

  10. C. Gunduz-Demir, “Digital pathology of cancer,” Technical University of Munich, Chair for Computer Aided Medical Procedures and Augmented Reality, Munich, Germany, Oct 2013.

  11. C. Gunduz-Demir, “Model-based segmentation for medical images,” MilSOFT Software Technologies, Ankara, Turkey, Oct 2013.

  12. C. Gunduz-Demir, “Digital pathology of cancer,” 7th International Symposium on Health Informatics and Bioinformatics, Medical Image Analysis Workshop, Urgup, Turkey, Apr 2012.

  13. C. Gunduz-Demir, “Tissue object patterns for segmentation in histopathological images,” 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies, Barcelona, Spain, Oct 2011.

  14. C. Gunduz-Demir, “Digital pathology of cancer,” Bilkent University, Department of Electrical and Electronics Engineering, Ankara, Turkey, Dec 2011.

  15. C. Gunduz-Demir, “Tissue image analysis for automated cancer diagnosis and grading,” Nanyang Technological University, School of Electrical and Electronic Engineering, Singapore, Apr 2009.

  16. C. Gunduz-Demir, “Tissue representation and analysis for automated cancer diagnosis and grading,” Bilkent University, Department of Molecular Biology and Genetics, Ankara, Turkey, Dec 2006.

  17. C. Gunduz-Demir, “Automated cancer diagnosis and grading on biopsies,” Middle East Technical University, Department of Computer Engineering, Ankara, Turkey, Nov 2006.

  18. C. Gunduz-Demir, “The cell-graphs of cancer,” University of Texas, MD Anderson Cancer Center, Houston, TX, Nov 2005.

  19. C. Gunduz-Demir, “The cell-graphs of cancer,” Bilkent University, Department of Computer Engineering, Ankara, Turkey, Jun 2005.

Others

  1. C.T. Sari, C. Sokmensuer, and C. Gunduz-Demir, “Image embedded segmentation: Uniting supervised and unsupervised objectives for segmenting histopathological images,” https://arxiv.org/abs/2001.11202.

  2. C. Demir and B.Yener, “Automated cancer diagnosis based on histopathological images: a systematic survey,” Technical report, Rensselaer Polytechnic Institute, Department of Computer Science, TR-05-09, Mar 2005.

  3. C. Demir, S.H. Gultekin, and B.Yener, “Spectral analysis of cell-graphs of cancer,” Technical report, Rensselaer Polytechnic Institute, Department of Computer Science, TR-04-17, Nov 2004.

  4. C. Demir, S.H. Gultekin, and B.Yener, “Learning the topological properties of brain tumors,” Technical report, Rensselaer Polytechnic Institute, Department of Computer Science, TR-04-14, Nov 2004.

  5. C. Gunduz, B. Yener, and S.H. Gultekin, “Learning the cell-graphs: Macroscopic modeling of brain tumors,” Technical report, Rensselaer Polytechnic Institute, Department of Computer Science, TR-03-12, Oct 2003.

  6. C. Gunduz and B. Yener, “Accuracy and sampling trade-offs for inferring Internet router graph,” Technical report, Rensselaer Polytechnic Institute, Department of Computer Science, TR-03-9, Jul 2003.

  7. C. Gunduz, M. Balman, and B. Yener, “Evasiveness of Internet topology,” Technical report, Rensselaer Polytechnic Institute, Department of Computer Science, TR-03-2, Mar 2003.

Theses

  1. C. Gunduz, “The cell-graphs of brain cancer,” Ph.D. thesis, Rensselaer Polytechnic Institute, Troy, NY, Dec 2005.

  2. C. Gunduz, “Value of representation in pattern recognition,” M.S. thesis, Bogazici University, Istanbul, Turkey, Jun 2001.