Detail

Keywords

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

Research group(s)

• Applied Quantum Computing Group
  Head: Laszlo Papp
  Research Area: Quantum imaging, radiomics and AI; Classic-Quantum adoption;
  Members:
  Sasan Moradi
  Laszlo Papp

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.