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Detail

Clemens P. Spielvogel
Clemens P. Spielvogel, MScData Scientist

Department of Biomedical Imaging and Image-guided Therapy (Division of Nuclear Medicine)
Position: PHD Student

ORCID: 0000-0002-4409-8324
T +43 1 +43 1 40400 55450
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); Oncology; Pattern Recognition, Automated

Research group(s)

Research interests

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

 

Selected publications

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.