I am an Assistant Professor at the University of Oulu, and I am also a member of the European Lab for Learning and Intelligent Systems (ELLIS).
The focus of my research is on Intelligent Medical Systems, and I develop new machine learning methods for medical applications.
Earlier, I was a post-doctoral fellow at the Finnish Center for Artificial Intelligence and Aalto University working with Prof. Samuel Kaski and Prof. Simo Särkkä.
Before that, 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. 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.
I was awarded a title of docent (i.e. habilitation) in Machine Learning for Medical Imaging on 01.02.2022.
Motivation. My group develops new AI methods for medical applications. The research that we are conducting is motivated by the following challenges that we either already face with medical AI, or will be facing in very near future:
My PhD thesis (best doctoral thesis of 2020 award): Download PDF.The full list of my papers can be found on Google Scholar. The list of selected projects:
Nguyen, K, Nguyen, H. H., & Tiulpin, A. (2021).
AdaTriplet: Adaptive Gradient Triplet Loss with Automatic Margin Learning for Forensic Medical Image Matching. (MICCAI 2022; top-13% of all papers)
Nguyen, H. H., Saarakkala, S., Blaschko, M. B., & Tiulpin, A. (2021).
CLIMAT: Clinically-Inspired Multi-Agent Transformers for Disease Trajectory Forecasting from Multi-modal Data. (ISBI 2022)
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.
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)
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.
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.