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Detail

Laszlo Papp
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

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.