Machine Learning Group

The main focus of our research group is in the development of state-of-the-art methods that solve numerous problems in clinical and biomedical imaging problems. We design Machine Learning algorithms with a special focus on Deep Learning to speed up and improve reliability of clinical decision making. Another aim that we pursue is in creating efficient methods for data analysis in basic and clinical research. Our current projects include automatic tissue and anatomical landmarks localization, learning from limited data using semi- and weakly-supervised learning, robustness enhancements of Deep Learning methods, disease severity assessment and other topics.

If you are interested in joining our group or looking for a topic for Bachelor’s or Master’s thesis, please, contact the PhD students or postdocs.



  • Osmo Tervonen, Professor, MD, PhD
  • Simo Saarakkala, Professor, PhD
  • Miika Nieminen, Professor, PhD
  • Aleksei Tiulpin, Post-doctoral Fellow, PhD


  • Aleksei Tiulpin, PhD
  • Neslihan Yalcin Bayramoglu, PhD

PhD Students

  • Egor Panfilov, MSc
  • Huy Hoang Nguyen, MSc
  • Santeri Rytky, MSc
  • Abu Mohammed Raisuddin, BSc

Research Assistants

  • Mustafa Al-Rubaye, BSc student


Recent publications:

  • Rytky, S. J., Tiulpin, A., Frondelius, T., Finnilä, M. A., Karhula, S. S., Leino, J., ... & Saarakkala, S. (2020). Automating three-dimensional osteoarthritis histopathological grading of human osteochondral tissue using machine learning on contrast-enhanced micro-computed tomography. Osteoarthritis and Cartlage 
  • Bayramoglu, N., Tiulpin, A., Hirvasniemi, J., Nieminen, M. T., & Saarakkala, S. (2020). Adaptive segmentation of knee radiographs for selecting the optimal ROI in texture analysis. Osteoarthritis and Cartilage.
  • Tiulpin, A., Finnilä, M., Lehenkari, P., Nieminen, H. J., & Saarakkala, S. (2020, February). Deep-learning for tidemark segmentation in human osteochondral tissues imaged with micro-computed tomography. In International Conference on Advanced Concepts for Intelligent Vision Systems (pp. 131-138). Springer, Cham.
  • Solovyev, R., Melekhov, I., Lesonen, T., Vaattovaara, E., Tervonen, O., & Tiulpin, A. (2020, February). Bayesian feature pyramid networks for automatic multi-label segmentation of chest X-rays and assessment of cardio-thoratic ratio. In International Conference on Advanced Concepts for Intelligent Vision Systems (pp. 117-130). Springer, Cham.
  • Panfilov, E., Tiulpin, A., Klein, S., Nieminen, M. T., Saarakkala, S. (2019). Improving Robustness of Deep Learning Based Knee MRI Segmentation: Mixup and Adversarial Domain Adaptation. In Proceedings of the IEEE International Conference on Computer Vision Workshops (pp. 0-0).
  • Tiulpin, A., Klein, S., Bierma-Zeinstra, S. M., Thevenot, J., Rahtu, E., van Meurs, J., ... & Saarakkala, S. (2019). Multimodal machine learning-based knee osteoarthritis progression prediction from plain radiographs and clinical data. Scientific Reports9(1), 1-11.
  • Tiulpin, A., Melekhov, I., Saarakkala, S. (2019). KNEEL: Knee Anatomical Landmark Localization Using Hourglass Networks. In Proceedings of the IEEE International Conference on Computer Vision Workshops (pp. 0-0). 
  • Tiulpin, A., Thevenot, J., Rahtu, E., Lehenkari, P., Saarakkala, S. (2018). Automatic knee osteoarthritis diagnosis from plain radiographs: A deep learning-based approach. Scientific reports8(1), 1727.
We also actively collavorate with multiple domestic and international institutions, including Erasmus Medical Center, Rotterdam (NL), KU Leuven (BE), University of Tampere (FI), University of Helsinki (FI), Aalto University (FI).  Our recent collaboration projects:
  • Wang, Johnny, et al. "Gray matter age prediction as a biomarker for risk of dementia." Proceedings of the National Academy of Sciences 116.42 (2019): 21213-21218.
  • Melekhov, Iaroslav, et al. "Dgc-net: Dense geometric correspondence network." 2019 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2019.
  • Rakhlin, Alexander, et al. "Breast tumor cellularity assessment using deep neural networks." Proceedings of the IEEE International Conference on Computer Vision Workshops. 2019.
  • Tiulpin, Aleksei, and Simo Saarakkala. "Automatic grading of individual knee osteoarthritis features in plain radiographs using deep convolutional neural networks." arXiv preprint arXiv:1907.08020 (2019).

Last updated: 10.6.2020