Aleksei Tiulpin

Aleksei Tiulpin

PhD

Post-doctoral fellow
Machine Learning for Medical Imaging

16 peer reviewed publications in international journals. The publications have been cited 166 times, of which the most cited publication accounts for 89 citations. The author has a h-index of 5.00. (Google Scholar)

Biography

I conduct research on Deep Learning for Medical Imaging. Specifically, I am interested in learning with limited data, noisy labels, self-supervised learning, uncertainty estimation, and anomaly detection.

Research interests

  • Machine Learning
  • Computer Vision
  • Medicine
  • Medical Imaging

Social media

Research groups

  • Post-doctoral fellow, Research Unit of Medical Imaging Physics and Technology

Selected publications

  • Tiulpin, Aleksei; Thevenot, Jérôme; Rahtu, Esa; Lehenkari, Petri; Saarakkala, Simo (2018) Automatic knee osteoarthritis diagnosis from plain radiographs : a deep learning-based approach. - Scientific Reports 8, 1727 . [Original] [Self-archived]
  • Tiulpin, A.; Melekhov, I.; Saarakkala, S. (2019) KNEEL: Knee anatomical landmark localization using hourglass networks. (Artikkeli tieteellisessä konferenssijulkaisussa). - 17th IEEE/CVF International Conference on Computer Vision Workshop, ICCVW 2019. IEEE International Conference on Computer Vision workshops. 352-361, 9022083. [Original] [Self-archived]
  • Tiulpin, A.; Klein, S.; Bierma-Zeinstra, S.M.A.; Thevenot, J.; Rahtu, E.; Meurs, J.; Oei, E.H.G.; Saarakkala, S. (2019) Multimodal Machine Learning-based Knee Osteoarthritis Progression Prediction from Plain Radiographs and Clinical Data. - Scientific reports 9 (1), 20038 . [Original] [Self-archived]
  • Panfilov, Egor; Tiulpin, Aleksei; Klein, Stefan; Nieminen, Miika T.; Saarakkala, Simo (2019) Improving Robustness of Deep Learning Based Knee MRI Segmentation: Mixup and Adversarial Domain Adaptation. (Artikkeli tieteellisessä konferenssijulkaisussa). - 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW) : 27th October- 2nd November 2019, Seoul, Korea. IEEE International Conference on Computer Vision workshops. 450-459. [Original] [Self-archived]
  • Melekhov, I.; Tiulpin, A.; Sattler, T.; Pollefeys, M.; Rahtu, E.; Kannala, J. (2019) DGC-Net: Dense geometric correspondence network. (Artikkeli tieteellisessä konferenssijulkaisussa). - 19th IEEE Winter Conference on Applications of Computer Vision, WACV 2019. IEEE Winter Conference on Applications of Computer Vision. 1034-1042, 8658868. [Original] [Self-archived]

Research visits

  • Erasmus MC, Rotterdam, The Netherlands
    1.3.2018 to 1.6.2018

Projects

Prediction and decision support systems for knee osteoarthritis

Strategic research project of the University of OuluFocus institute: Infotech OuluFaculty: Faculty of Medicine (FoM)  

Future Artificial Intelligence-tailored Hospital (FAITH)

Current Artificial Intelligence (AI)-based diagnostic tools are developed using curated and clean datasets, therefore their applicability to the re