Detail

Keywords

Artificial Intelligence

Research group(s)

• QIMP group
  Head: Thomas Beyer, PhD, MBA
  Members:
  • Lalith Kumar Shiyam Sundar
  • Jacobo Cal-Gonzalez
  • Ivo Rausch
  • Laszlo Papp
  • Thomas Beyer, PhD, MBA
  • July Alejandra Gonzalez Valladares
  • David Iommi
  • Alexander Berger
  • Martin Lyngby Lassen
  • Hunor Kertesz
  • Denis Krajnc

Research interests

Machine learning, image processing, personalized medicine, tumour characterization

Techniques, methods & infrastructure

Techniques, Methods: In vivo feature engineering, Embedded feature selection, Stacked ensemble learning, Monte Carlo cross-validation

Infrastructure: high-performance cloud computing (ITSC)

Selected publications

1. Papp, L. et al., 2017. Glioma survival prediction with the combined analysis of in vivo 11C-MET-PET, ex vivo and patient features by supervised machine learning. Journal of Nuclear Medicine, p.jnumed.117.202267. Available at: http://dx.doi.org/10.2967/jnumed.117.202267.
2. Lützen, U. et al., 2016. A study on the value of computer-assisted assessment for SPECT/CT-scans in sentinel lymph node diagnostics of penile cancer as well as clinical reliability and morbidity of this procedure. Cancer Imaging, 16(1). Available at: http://dx.doi.org/10.1186/s40644-016-0087-z.
3. Perlaki, G. et al., 2016. Validation of an automated morphological MRI-based 123I-FP-CIT SPECT evaluation method. Parkinsonism & Related Disorders, 29, pp.24-29. Available at: http://dx.doi.org/10.1016/j.parkreldis.2016.06.001.
4. Salamon, J. et al., 2015. Nerve Sheath Tumors in Neurofibromatosis Type 1: Assessment of Whole-Body Metabolic Tumor Burden Using F-18-FDG PET/CT M. Katoh, ed. PLOS ONE, 10(12), p.e0143305. Available at: http://dx.doi.org/10.1371/journal.pone.0143305.