Neslihan Bayramoglu

Currently, I am working as a senior research fellow at the Research Unit of Health Sciences and Technology (HST), University of Oulu, Finland. I was granted the title of Docent in 2022.

My work spans a broad spectrum in artificial intelligence machine learning and computer vision, covering subjects as diverse as deep learning, computer assisted diagnosis, image processing, shape analysis, segmentation, classification, 3D image retrieval, and facial expression recognition

I have extensive experience in applying modern machine learning approaches in medicine. Together with my co-authors, I contributed to establishing one of the earliest connections between deep learning and histopathology image analysis, particularly in the context of breast cancer imaging. Our work includes contributions to detection [1], transfer learning [2], and virtual tissue staining using Generative Adversarial Networks (GANs) [3].

My recent research efforts have focused on the machine learning/deep learning based analysis of routinely collected health data, including imaging data, demographics, and clinical information, with a specific emphasis on musculoskeletal diseases, particularly osteoarthritis (OA) [4, 5, 6, 7, 8].

I have over a decade of teaching experience, which I thoroughly enjoyed. Currently, I am teaching a graduate-level introduction to machine learning course offered at the University of Oulu. Additionally, I am actively supervising M.Sc. students and doctoral researchers in machine learning.

• [1] N. Bayramoglu, J. Kannala, and J. Heikkilä, “Deep learning for magnification independent breast cancer histopathology image classification,” in Pattern Recognition (ICPR), 2016 23rd International Conference on, pp. 2440–2445, IEEE, 2016.
• [2] N. Bayramoglu and J. Heikkilä, “Transfer learning for cell nuclei classification in histopathology images,” in Computer Vision–ECCV 2016 Workshops, pp. 532–539, Springer, 2016.
• [3] N. Bayramoglu, M. Kaakinen, L. Eklund, and J. Heikkila, “Towards virtual h&e staining of hyperspectral lung histology images using conditional generative adversarial networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 64–71, 2017.
• [4] N. Bayramoglu, M. Englund, I. K. Haugen, M. Ishijima, and S. Saarakkala, “Deep learning for predicting pro- gression of patellofemoral osteoarthritis based on lateral knee radiographs, demographic data and symptomatic assessments,” Methods of Information in Medicine, 2024.
• [5] N. Bayramoglu, A. Tiulpin, J. Hirvasniemi, M. T. Nieminen, and S. Saarakkala, “Adaptive segmentation of knee radiographs for selecting the optimal roi in texture analysis,” Osteoarthritis and cartilage, 2020.
• [6] N. Bayramoglu, M. T. Nieminen, and S. Saarakkala, “Machine learning based texture analysis of patella from x- rays for detecting patellofemoral osteoarthritis,” International journal of medical informatics, vol. 157, p. 104627, 2022.
• [7] N. Bayramoglu, M. T. Nieminen, and S. Saarakkala, “Automated detection of patellofemoral osteoarthritis from knee lateral view radiographs using deep learning: Data from the multicenter osteoarthritis study (most),” Os- teoarthritis and Cartilage, 2021.
• [8] N. Bayramoglu, M. T. Nieminen, and S. Saarakkala, “A lightweight cnn and joint shape-joint space (js2) descriptor for radiological osteoarthritis detection,” in Medical Image Understanding and Analysis, pp. 331–345, 2020.

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