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
- Christian Doppler Laboratory for Applied Metabolomics
- 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
Using biological data to derive medical 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 (Python)
- Analysis of Omics data
- 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.
- Schnoell, J. et al., 2020. Transcription factors CP2 and YY1 as prognostic markers in head and neck squamous cell carcinoma: analysis of The Cancer Genome Atlas and a second independent cohort. Journal of Cancer Research and Clinical Oncology, 147(3), pp.755–765. Available at: http://dx.doi.org/10.1007/s00432-020-03482-6.
- 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.
- 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.
- Andreana M. et al., 2021. Towards quantitative in vivo label-free tracking of lipid distribution in a zebrafish cancer model. Front. Cell Dev. Biol. Available at https://www.frontiersin.org/articles/10.3389/fcell.2021.675636