Aleksei Tiulpin, PhD

Post-doctoral fellow
Finnish Center for Artificial Intelligence & Aalto University

Konemiehentie 2
02150 Espoo

Email: firstname [dot] lastname [at] aalto [dot] fi

Google Scholar

About me

I am post-doctoral fellow at the Finnish Center for Artificial Intelligence and Aalto University working with Prof. Samuel Kaski. I am also a member of the European Lab for Learning and Intelligent Systems (ELLIS). Previously, I was a post-doctoral fellow at KU Leuven, working with Prof. dr. Matthew Blaschko.

I earned my PhD during 2017-2020 (graduated with distinction) at the University of Oulu, Finland. My advisors were Prof. Simo Saarakkala, PhD (primary), Dr. Jérôme Thevenot, PhD, and Prof. Esa Rahtu. After gradiation, received the best doctoral thesis award of the Faculty of Medicine, University of Oulu.

In addition to my academic activities, I am a co-founder and CTO of Ailean Technologies Ltd.

Research interests

I am currently working on principled methods for Bayesian posterior approximation with Deep Neural Nets. I am also intersted in semi-supervised, self-supervised, and active learning. My main application area is medical imaging. Previously, I have published various applied papers on knee osteoarthritis, digital pathology, brain imaging and other fields.

In my research, I investigate how to build medical AI systems that

Current PhD students

I am privelleged to supervise the following PhD students at the University of Oulu:


I have at total of 19 published / in press international peer-reviewed papers. I also have several patents.

My PhD thesis: Download PDF.

Full list of my papers can be found on Google Scholar. The list of selected projects:

Raisuddin, A. M., Vaattovaara, E., Nevalainen, M., Nikki, M., Järvenpää, E., Makkonen, K., ... & Tiulpin, A. (2020). Deep Learning for Wrist Fracture Detection: Are We There Yet?. arXiv preprint arXiv:2012.02577.
[Link] [Code]

Nguyen, H. H., Saarakkala, S., Blaschko, M. B., & Tiulpin, A. (2020). Semixup: In- and Out-of-Manifold Regularization for Deep Semi-Supervised Knee Osteoarthritis Severity Grading From Plain Radiographs. IEEE transactions on medical imaging, 39(12), 4346–4356.
[Link] [Code]

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. 450-459)
[Link] [Code]

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. 352-361)
[Link] [Code]

Tiulpin, A., Klein, S., Bierma-Zeinstra, S. M., Thevenot, J., Rahtu, E., van Meurs, J., Oei, E. & Saarakkala, S. (2019). Multimodal machine learning-based knee osteoarthritis progression prediction from plain radiographs and clinical data. Scientific reports, 9(1), 1-11.
[Link] [Code]

Tiulpin, A., Thevenot, J., Rahtu, E., Lehenkari, P., & Saarakkala, S. (2018). Automatic knee osteoarthritis diagnosis from plain radiographs: a deep learning-based approach. Scientific reports, 8(1), 1-10.
[Link] [Code]