Skip to main content

Detail

Roxane Licandro
Dipl.-Ing. Roxane Licandro, BSc

Department of Biomedical Imaging and Image-guided Therapy
Position: Research Assistant

ORCID: 0000-0001-9066-4473
T +43 1 40400 73724
roxane.licandro@meduniwien.ac.at

Further Information

Keywords

Artificial Intelligence; Pattern Recognition, Automated; Spatio-Temporal Analysis

Research group(s)

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, bone lesion prediction in multiple myeloma and in the field of automatic MRD assessment in childhood leukaemia. 

 

Techniques, methods & infrastructure

  • Diffeomorphic registration
  • Machine learning and statistical pattern analysis
  • Spatio temporal modelling and anomaly prediction
  • Computer Vision

Selected publications

  1. Sobotka D., Licandro R., Ebner M., Schwartz E., Vercauteren T., Ourselin S., Kasprian G., Prayer D., Langs G., 2019. Reproducibility of Functional Connectivity Estimates in Motion Corrected Fetal fMRI. Lecture Notes in Computer Science, pp.123–132. Available at: http://dx.doi.org/10.1007/978-3-030-32875-7_14.
  2. Licandro R., Hofmanninger J., Perkonigg M., Röhrich S., Weber M.-A., Wennmann M., Kintzele L., Piraud M., Menze B., Langs G., "Asymmetric Cascade Networks for Focal Bone Lesion Prediction in Multiple Myeloma", International Conference on Medical Imaging with Deep Learning (MIDL), July 2019. https://arxiv.org/abs/1907.13539.
  3. Licandro R. and Schlegl T., Reiter M., Diem M., Dworzak M., Schumich A., Langs G., Kampel M., 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.
  4. Licandro R., Nenning K.-H., Schwartz E., Kollndorfer K., Bartha-Doering L., Liu H., Langs G., 2017. Assessing Reorganisation of Functional Connectivity in the Infant Brain. Lecture Notes in Computer Science, pp.14–24. Available at: http://dx.doi.org/10.1007/978-3-319-67561-9_2.
  5. Licandro R., Langs G., Kasprian G., Sablatnig R. Prayer D., Schwartz E, " Longitudinal Atlas Learning for Fetal Brain Tissue Labeling using Geodesic Regression", Woman in Computer Vision (WiCV) Workshop at the IEEE Conference on Computer Vision and Pattern Recognition, July 2016.