Department of Biomedical Imaging and Image-guided Therapy
Position: Research Associate (Postdoc)
ORCID: 0000-0001-9066-4473
T +43 1 40400 73723
roxane.licandro@meduniwien.ac.at
Keywords
Artificial Intelligence; Pattern Recognition, Automated; Spatio-Temporal Analysis
Research group(s)
- Computational Imaging Research Lab
Head: Georg Langs
Research Area: Research at CIR is roughly grouped around 3 research lines: Machine Learning & Neuroimaging, Computer Aided Diagnosis and Quantification, Computer Vision and Pattern Recognition
Members:
Research interests
My main research focus lies on finding new ways to computationally model and predict dynamic processes in space and over time, especially in the field of fetal and paediatric brain development, functional brain networks and plasticity, sudden infant death syndrom (SIDS) and postmortem brain analysis, pediatric and fetal brain development, functional connectivity and plasticity and statistical pattern analysis for children cancer research.
Techniques, methods & infrastructure
- Diffeomorphic registration
- Machine learning and statistical pattern analysis
- Spatio temporal modelling and anomaly prediction
- Medical Computer vision
- Uncertainty and Interpretable AI
- High resolution reconstruction and motion correction
- Shape and Surface-based Analysis
Grants
- Mathematical modelling and simulations for next generations of ultrasound devices (2025)
Source of Funding: CDG (Christian Doppler Research Association), Mathematics, Computer Sciences, Electronic Engineering
Coordinator of the collaborative project
Selected publications
- Taymourtash, A. et al. (2025) ‘Measuring the effects of motion corruption in fetal <scp>fMRI</scp>’, Human Brain Mapping, 46(2). Available at: https://doi.org/10.1002/hbm.26806.
- Gutwein, S. et al. (2024) ‘FISHing in Uncertainty: Synthetic Contrastive Learning for Genetic Aberration Detection’, Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, pp. 23–33. Available at: https://doi.org/10.1007/978-3-031-73158-7_3.
- Payette, K. et al. (2024) ‘Multi-Center Fetal Brain Tissue Annotation (FeTA) Challenge 2022 Results’, IEEE Transactions on Medical Imaging, pp. 1–1. Available at: https://doi.org/10.1109/tmi.2024.3485554.
- R. Licandro and T. Schlegl, M. Reiter, M. Diem, M. Dworzak, A. Schumich, G. Langs, M. Kampel, 2018. WGAN Latent Space Embeddings for Blast Identification in Childhood Acute Myeloid Leukaemia. 2018 24th International Conference on Pattern Recognition (ICPR). Available at: http://dx.doi.org/10.1109/ICPR.2018.8546177.
- Sobotka, D. et al. (2022) ‘Motion correction and volumetric reconstruction for fetal functional magnetic resonance imaging data’, NeuroImage, 255, p. 119213. Available at: https://doi.org/10.1016/j.neuroimage.2022.119213.