Laszlo Papp
Center for Medical Physics and Biomedical Engineering
Position: Research Associate (Postdoc)
ORCID: 0000-0002-9049-9989
T +43 1 40400 72350
laszlo.papp@meduniwien.ac.at
Keywords
Artificial Intelligence; Data Mining; Image Interpretation, Computer-Assisted; Image Processing, Computer-Assisted
Research group(s)
- QIMP group
Head: Thomas Beyer, PhD, MBA
Research Area: Quantitative, combined imaging (PET/CT, PET/MR, SPECT/CT); Supporting clinical adoption of fully integrated PET/MRI; Image-based phenotyping and texture analysis
Members:
Research interests
Quantum computing, machine learning, image processing, personalized medicine, tumour characterization
Techniques, methods & infrastructure
Techniques, Methods:
- In vivo feature engineering
- Radiomics, holomics
- Ensemble learning
- Quantum machine learning
Infrastructure:
- ITSC high-performance computing (64 CPU cores)
- Quantum simulation HPC (256 CPU cores, 7 TByre RAM)
Grants
- Foundations of a Quantum Computational Lab at the CMPBME (2019)
Source of Funding: Medical University of Vienna, Focus XL Grant Scheme
Principal Investigator
Selected publications
- Papp, L. et al., 2020. Supervised machine learning enables non-invasive lesion characterization in primary prostate cancer with [68Ga]Ga-PSMA-11 PET/MRI. European Journal of Nuclear Medicine and Molecular Imaging. Available at: http://dx.doi.org/10.1007/s00259-020-05140-y.
- Papp, L. et al., 2018. Optimized Feature Extraction for Radiomics Analysis of 18F-FDG PET Imaging. Journal of Nuclear Medicine, 60(6), pp.864–872. Available at: http://dx.doi.org/10.2967/jnumed.118.217612.
- Papp, L. et al., 2017. Glioma Survival Prediction with Combined Analysis of In Vivo11C-MET PET Features, Ex Vivo Features, and Patient Features by Supervised Machine Learning. Journal of Nuclear Medicine, 59(6), pp.892–899. Available at: http://dx.doi.org/10.2967/jnumed.117.202267.
- Papp, L. et al., 2018. Personalizing Medicine Through Hybrid Imaging and Medical Big Data Analysis. Frontiers in Physics, 6. Available at: http://dx.doi.org/10.3389/fphy.2018.00051.