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:
  Lalith Kumar Shiyam Sundar
  July Alejandra Gonzalez Valladares
  Hunor Kertesz
  Denis Krajnc
  Thomas Beyer, PhD, MBA
  David Iommi
  Sasan Moradi
  Ivo Rausch
  Christoph Brandner
  Sebastian Gutschmayer
  Laszlo Papp

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

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