Department of Biomedical Imaging and Image-guided Therapy (Division of Nuclear Medicine)
Position: Research Associate (Postdoc)
ORCID: 0000-0002-4409-8324
T +43 1 40400 72350
clemens.spielvogel@meduniwien.ac.at
Keywords
Amyloidosis; Artificial Intelligence; Automatic Data Processing; Cardiac Imaging Techniques; Computational Biology; Data Mining; Decision Making, Computer-Assisted; Electronic Health Records; Image Interpretation, Computer-Assisted; Image Processing, Computer-Assisted; Medical Informatics; Nuclear Medicine; Pattern Recognition, Automated
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
- Imaging marker quantification
- Machine learning
- Vision-based deep learning
- Explainable artificial intelligence
- Automated image segmentation
- Imaging / non-imaging data integration
Selected publications
- Spielvogel, C.P. et al. (2024) ‘Diagnosis and prognosis of abnormal cardiac scintigraphy uptake suggestive of cardiac amyloidosis using artificial intelligence: a retrospective, international, multicentre, cross-tracer development and validation study’, The Lancet Digital Health, 6(4), pp. e251–e260. Available at: https://doi.org/10.1016/s2589-7500(23)00265-0.
- Haberl, D. et al. (2024) ‘Multicenter PET image harmonization using generative adversarial networks’, European Journal of Nuclear Medicine and Molecular Imaging. Available at: https://doi.org/10.1007/s00259-024-06708-8.
- 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, 50(2), pp. 546–558. Available at: https://doi.org/10.1007/s00259-022-05973-9.
- 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: https://doi.org/10.1016/j.neunet.2023.08.026.
- 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, 48(6), pp. 1795–1805. Available at: https://doi.org/10.1007/s00259-020-05140-y.