Department of Biomedical Imaging and Image-guided Therapy (Division of Nuclear Medicine)
Position: Research Associate (Postdoc)
ORCID: 0000-0002-4409-8324
T +43 1 +43 1 40400 72350
clemens.spielvogel@meduniwien.ac.at
Keywords
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); Nuclear Medicine; Oncology; Pattern Recognition, Automated
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: - Christian Doppler Laboratory for Applied Metabolomics
Head:
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.
Members:
Research interests
Discovery, development, and clinical assessment of novel biomarkers and clinical decision support systems.
Focus areas include nuclear medicine, oncological, and cardiovascular diseases.
Techniques, methods & infrastructure
- Vision-based Deep Learning
- Traditional Machine Learning
- Explainable Artificial Intelligence (XAI)
- Image Segmentation
- Scientific Software Development
- Quantitative Imaging Markers
- Imaging / Non-Imaging Data Integration
Selected publications
- 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.
- 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: http://dx.doi.org/10.1016/j.neunet.2023.08.026.
- 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.