Amyloidosis; Artificial Intelligence; Automatic Data Processing; Computational Biology; Data Mining; Decision Making, Computer-Assisted; Image Interpretation, Computer-Assisted; Image Processing, Computer-Assisted; Medical Informatics; Neural Networks (Computer); Oncology; Pattern Recognition, Automated
- 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
- Christian Doppler Laboratory for Applied Metabolomics
Research Area: We investigate ways to better characterize tumors using non-invasive imaging techniques. This is important because tumours are constantly changing through mutations. In this way, an individual therapy should be possible and its success should be continuously monitored.
Using biomedical data to derive knowledge through predictive modelling.
Integrating data from various sources including hybrid imaging and high-throughput sequencing for building machine learning models in oncology and cancer biology.
Techniques, methods & infrastructure
- Machine Learning
- Vision-based Deep Learning
- Explainable Artificial Intelligence (XAI)
- Scientific Software Development
- Analysis of Omics data
- Spielvogel, C.P. et al. (2022) ‘Radiogenomic markers enable risk stratification and inference of mutational pathway states in head and neck cancer’, European Journal of Nuclear Medicine and Molecular Imaging. Available at: http://dx.doi.org/10.1007/s00259-022-05973-9.
- Krajnc, D. et al. (2022) ‘Automated data preparation for in vivo tumor characterization with machine learning’, Frontiers in Oncology, 12. Available at: http://dx.doi.org/10.3389/fonc.2022.1017911.
- Nopp, S. et al. (2022) ‘Bleeding Risk Assessment in End-Stage Kidney Disease: Validation of Existing Risk Scores and Evaluation of a Machine Learning-Based Approach’, Thrombosis and Haemostasis, 122(09). Available at: http://dx.doi.org/10.1055/a-1754-7551.
- Krajnc, D. et al. (2021) ‘Breast Tumor Characterization Using [18F]FDG-PET/CT Imaging Combined with Data Preprocessing and Radiomics’, Cancers, 13(6), p. 1249. Available at: http://dx.doi.org/10.3390/cancers13061249.
- Papp, L. et al. (2021) ‘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.