Journal papers

  1. Iheme, L. O., Önal, S., Erdem, Y. S., Uçar, M., Yalçın-Özuysal, Ö., Pesen-Okvur, D., Töreyin, B. U., Ünay, D. Collection: Wound Healing Assay Dataset (WHAD) and Cell Adhesion and Motility Assay Dataset (CAMAD), IEEE Data Descriptions, doi: 10.1109/IEEEDATA.2024.3481394.
  2. Erdem, Y. S., Iheme, L. O., Uçar, M., Özuysal, Ö. Y., Balıkçı, M., Morani, K., Töreyin, B. U., Ünay, D. Novel Neural Style Transfer based data synthesis method for phase-contrast wound healing assay images. Biomedical Signal Processing and Control, 96, 106514, 2024.
  3. Morani, K., Ayana, E. K., Kollias, D., & Unay, D. COVID‐19 Detection from Computed Tomography Images Using Slice Processing Techniques and a Modified Xception Classifier. International Journal of Biomedical Imaging, 2024(1), 9962839.
  4. Ucar, M., Iheme, L. O., Onal, S., Pesen-Okvur, D., Yalcin-Ozuysal, O., Toreyin, B. U., & Unay, D. Blank Frame and Intensity Variation Distortion Detection and Restoration Pipeline for Phase-Contrast Microscopy Time-Lapse Images. Electrica, 24(1), 2024.
  5. Morani, K., Unay, D., Deep Learning Based Automated COVID-19 Classification from Computed Tomography Images, Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 11(6), 2145-2160, 2023.
  6. Yao, J., Xing, J., Zheng, F., Li, Z., Li, S., Xu, X., Unay, D., Song, Y.M., Yang, F., Wu, A., Dual-infinite coordination polymer-engineered nanomedicines for dual-ion interference-mediated oxidative stress-dependent tumor suppression, Materials Horizons, 2023.
  7. Argunşah, A. Ö., Erdil, E., Ghani, M. U., Cortés, Y. R., Hobbiss, A. F., Karayannis, T., Çetin, M., Israely, I., Ünay, D., An interactive time-series analysis software for dendritic spines, Scientific Reports, 12,12405, 2022.
  8. Unay, D., Deep Learning based Automatic Grading of Bi-Colored Apples using Multispectral Images, Multimedia Tools and Applications, 81, 38237–38252, 2022.
  9. Soyak, R., Navruz, E., Ersoy, E.O., Cruz, G., Prieto, C., King, A.P., Unay, D., Oksuz, I., Channel Attention Networks for Robust MR Fingerprint Matching, IEEE Transactions on Biomedical Imaging, 69(4), 1398-1405, 2022.
  10. Ayanzadeh, A., Ozuysal, O. Y., Okvur, D. P., Önal, S., Töreyin, B. U., Unay, D., Improved Cell Segmentation Using Deep Learning in Label-Free Optical Microscopy Images, Turkish Journal of Electrical Engineering & Computer Sciences, 29: 2855–2868, 2021.
  11. Erdem, Y. S., Ozuysal, O. Y., Okvur, D. P., Töreyin, B. U., Unay, D., An Image Segmentation Method for Wound Healing Assay Images, Natural and Applied Sciences Journal, 4(1), 30-37, 2021.
  12. Ünay, D, Harmankaya, I, Oksuz, I, Cubuk, R, Çelik, L, Kadipasaoglu, K, Model-free Automatic Segmentation of the Aortic Valve in Multislice Computed Tomography Images, PAJES, 27(2), 122-128, 2021.
  13. Comparative Analysis of Different Deep Learning Techniques for Automated Classification of Fruits and Vegetables, Unay, D., Turkish Studies-Applied Sciences, 15(4), 533-544, 2020.
  14. SSNOMBACTER: A collection of scattering-type Scanning Near-Field Optical Microscopy and Atomic Force Microscopy images of bacterial cells, M. Lucidi; D.E. Tranca; L. Nichele; D. Unay; G.A. Stanciu; P. Visca; A.M. Holban; R. Hristu; G. Cincotti; S.G. Stanciu, GigaScience, 9(11), 1-12, 2020.
  15. Rubens, U., Mormont, R., Baecker, V., Michiels, G., Paavolainen, L., Ball, G., Unay, D., Pavie, B., Chessel, A., Scholz, L., Maska, M., Hoyoux, R., Vandaele, R., Stanciu, S., Golani, O., Sladoje, N., Paul, P., Marée, R., Tosi, S., BIAFLOWS: A collaborative framework to benchmark bioimage analysis workflows, Patterns, 1(3), 100040, 2020. You can also check the interview in Prelights.
  16. Yalciner, B.Z., Kandemir, M., Taskale, S., Tepe, S.M., Unay, D., “Modified Visual MR Rating Scale for Evaluation of Patients with Forgetfulness” Canadian Journal of Neurological Sciences, 46 (1), 71-78, 2019.
  17. Rada, L., Kilic, B., Erdil, E., Ramiro-Cortez, Y., Israely, I., Unay, D., Cetin, M., Argunsah, A.O., “Tracking-assisted Detection of Dendritic Spines in Time Lapse Microscopic Images” Neuroscience, 394, 198-205, 2018.
  18. Unay, D., Stanciu, G.S., “An evaluation on the robustness of five popular keypoint descriptors to image modifications specific to laser scanning microscopy” IEEE Access, 6, 40154-40164, 2018.
  19. Müller, H., Unay, D., “Retrieval from and Understanding of Large-Scale Multi-Modal Medical Datasets: A Review”, IEEE Transactions on Multimedia, 19(9), 2093-2104, 2017.
  20. Erdil, E., Ghani, M.U., Rada, L., Argunsah, A.O., Unay, D., Tasdizen, T., Cetin, M., “Nonparametric Joint Shape and Feature Priors for Image Segmentation”, IEEE Transactions on Image Processing, 26(11), 5312-5323, 2017.
  21. Carass, A., et al., “Longitudinal Multiple Sclerosis Lesion Segmentation: Resource and Challenge” NeuroImage, 148, 77-102, 2017.
  22. Ghani, M.U., Mesadi, F., Kanik, S.D., Argunsah, A.O., Hobbiss, A.F., Israely, I., Unay, D., Tasdizen, T., Cetin, M., “Dendritic Spine Classification using Shape and Appearance Features based on Two-Photon Microscopy”, Journal of Neuroscience Methods, 279, 13-21, 2017.
  23. Cash, D.M., et al., “Assessing atrophy measurement techniques in dementia: Results from the MIRIAD atrophy challenge”, NeuroImage, 123, 149-164, 2015.
  24. Rudyanto, R.D., et al., “Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: The VESSEL12 study”, Medical Image Analysis, 18 (7), 1217-1232, 2014.
  25. Unay, D., Ozturk, C., Stone, M., “Single Syllable Tongue Motion Analysis Using Tagged Cine MRI”, Computer Methods in Biomechanics and Biomedical Engineering, 17 (8), 853-864, 2014.
  26. Kirişli, HA., et al., “Standardized Evaluation Framework for Evaluating Coronary Artery Stenosis Detection, Stenosis Quantification and Lumen Segmentation Algorithms in Computed Tomography Angiography”, Medical Image Analysis, 17 (8), 859-876, 2013.
  27. Kaya, M., Unay, D., “DressBoard: An Embedded System for Virtual Garment Trial”, The Journal of Signal Processing Systems, 73, 143-152, 2013.
  28. Unay, D., “Local and Global Volume Changes of Subcortical Brain Structures from Longitudinally Varying Neuroimaging Data for Dementia Identification”, Computerized Medical Imaging and Graphics, 36, 464-473, 2012.
  29. Ozogur-Akyuz, S., Unay, D., Smola, A., “Guest editorial: model selection and optimization in machine learning”, Machine Learning, 85 (1-2), 1-2, 2011.
  30. Unay, D., Gosselin, B., Kleynen, O, Leemans, V., Destain, M.-F., Debeir, O, “Automatic Grading of Bi-Colored Apples by Multispectral Machine Vision”, Computers and Electronics in Agriculture, 75(1), 204-212, 2011.
  31. Uzunbas, G., Soldea, O., Unay, D., Cetin, M., Unal, G., Ercil, A., Ekin, A., “Coupled Non-Parametric Shape and Moment-Based Inter-Shape Pose Priors for Multiple Basal Ganglia Structure Segmentation,” , IEEE Trans. Medical Imaging, 29(12), 1959-1978, 2010.
  32. Unay, D., Ekin, A., Jasinschi, R., “Local Structure-based Region-of-Interest Retrieval in Brain MR Images”, IEEE Trans. Information Technology in Biomedicine, 14(4), 897-903, 2010.
  33. Unay, D., Gosselin, B., “Stem and Calyx Recognition on ‘Jonagold’ Apples by Pattern Recognition”, Journal of Food Engineering, 78(2), 597-605, 2007.
  34. Unay, D., Gosselin, B., “Automatic Defect Segmentation of ‘Jonagold’ Apples on Multi-Spectral Images: A Comparative Study”, Journal of Postharvest Biology and Technology, 42(3), 271-279, 2006.