
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 interests
Quantum computing, machine learning, image processing, personalized medicine, tumour characterization
Techniques, methods & infrastructure
Techniques, Methods:
- Quantum machine learning
- Quantum image analysis
- Quantum simulators and NISQs
- In vivo feature engineering
- Radiomics, holomics
- Ensemble learning
Infrastructure:
- 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
- Moradi, S. et al. (2022) ‘Clinical data classification with noisy intermediate scale quantum computers’, Scientific Reports, 12(1). Available at: http://dx.doi.org/10.1038/s41598-022-05971-9.
- 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, 48(6), pp. 1795–1805. 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 Vivo 11C-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.