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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 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

 

Selected publications

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