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Laszlo Papp
Laszlo Papp, PhDI dedicate my professional career to advance personalized medicine throughout proposing novel AI methodologies in both classic and quantum computing fields to consolidate - and thereby - to make AI clinically-adoptable

Center for Medical Physics and Biomedical Engineering
Position: Research Associate (Postdoc)

ORCID: 0000-0002-9049-9989
T +43 1 40400 39224
laszlo.papp@meduniwien.ac.at

Further Information

Keywords

Artificial Intelligence; Data Mining; Image Interpretation, Computer-Assisted; Image Processing, Computer-Assisted

Research group(s)

Research interests

Quantum computing, machine learning, image processing, personalized medicine, tumour characterization

Techniques, methods & infrastructure

Techniques, Methods:

  • Quantum machine learning and deep learning
  • Quantum image analysis
  • Quantum simulators and NISQs
  • Biomorphic computing
  • In vivo feature engineering
  • Radiomics, holomics
  • Ensemble learning


Infrastructure:

  • Quantum simulation HPC (256 CPU cores, 7 TByre RAM)

Grants

  • Quantum Image Analysis (2022)
    Source of Funding: Medical University of Vienna, Focus M Grant Scheme
    Principal Investigator
  • Quantum Image Analysis (QIA) (2022)
    Source of Funding: Medical University of Vienna, Focus-M
    Principal Investigator
  • 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. (2023) ‘Error mitigation enables PET radiomic cancer characterization on quantum computers’, European Journal of Nuclear Medicine and Molecular Imaging [Preprint]. Available at: http://dx.doi.org/10.1007/s00259-023-06362-6.
  2. Papp, L. et al. (2023) ‘DEBI-NN: Distance-encoding biomorphic-informational neural networks for minimizing the number of trainable parameters’, Neural Networks, 167, pp. 517–532. Available at: https://doi.org/10.1016/j.neunet.2023.08.026.
  3. Grahovac, M. et al. (2023) ‘Machine learning predictive performance evaluation of conventional and fuzzy radiomics in clinical cancer imaging cohorts’, European Journal of Nuclear Medicine and Molecular Imaging, 50(6), pp. 1607–1620. Available at: https://doi.org/10.1007/s00259-023-06127-1.
  4. Moradi, S. et al. (2022) ‘Clinical data classification with noisy intermediate scale quantum computers’, Scientific Reports, 12(1). Available at: https://doi.org/10.1038/s41598-022-05971-9.
  5. 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: https://doi.org/10.1007/s00259-020-05140-y.