Aleksei Tiulpin, PhD

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

Konemiehentie 2
02150 Espoo
Finland

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

LinkedIn
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GitHub
Kaggle


About me


I am post-doctoral fellow at the Finnish Center for Artificial Intelligence and Aalto University working with Prof. Samuel Kaski and Prof. Simo Särkkä. 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 Faculty of Medicine, University of Oulu, Finland. My PhD thesis was nominated the best doctoral thesis of the year 2020. My advisors were Prof. Simo Saarakkala, PhD (primary), Dr. Jérôme Thevenot, PhD, and Prof. Esa Rahtu.

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

Research interests


I am currently working on AI-assisted design in the context of medical aplications. Generally, I am interested in Bayesian methods, semi-supervised, self-supervised, and active learning. My main application area is medical imaging. My translational contributions are in the fields of on musculoskeletal disorders, breast cancer, and dementia.

My research agenda is to 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:

Publications


I have over 20 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:


Tiulpin A. & Blaschko M.B (2021) Greedy Bayesian Posterior Approximation with Deep Ensembles. arXiv preprint arXiv:2105.14275
[Link] [Code]



Nguyen, H. H., Saarakkala, S., Blaschko, M. B., & Tiulpin, A. (2021). DeepProg: A Transformer-based Framework for Predicting Disease Prognosis. arXiv preprint arXiv:2104.03642.
[Link]



Raisuddin, A. M., Vaattovaara, E., Nevalainen, M., Nikki, M., Järvenpää, E., Makkonen, K., ... & Tiulpin, A. (2020). Critical Evaluation of Deep Neural Networks for Wrist Fracture Detection. Scientific reports, 11(1), 1-11.
[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]



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]