Diagnostik & Forschungszentrum für Molekulare BioMedizin

Information Science and Machine Learning

Research focus
  • Data- Analytics, Interoperability, Quality and Provenance
  • Digital Microscopy and Computational Pathology (together with K. Zatloukal)
  • Explainable AI and Human-AI Interaction (together with A. Holzinger)
Most exciting results
  • BIBBOX, an App store for life science, based on docker Containers
  • RD-Connect Biobank and Registry Finder (RD = rare diseases)
  • Kandinsky Patterns, an exploration tool for explainable AI in medical imaging
  • Analysis of biomedical data with multilevel glyphs
    BMC Bioinformatics, 2014
  • The RD-Connect Registry & Biobank Finder
    European Journal of Human Genetics, 2018
  • Causability and explainabilty of artificial intelligence in medicine
    Wiley Interdisciplinary Reviews, 2019
  • Visualization of Histopathological Decision Making Using a Roadbook Metaphor
    23rd International Conference Information Visualisation (IV), 2019
  • Kandinsky Patterns: An open toolbox for creating explainable machine learning challenges
    33rdAnnual Conference on Neural Information Processing Systems, NeurIPS, 2019
Future plans
  • Interoperability and „FAIRification“ for the Human Exposome Assessment Platform (HEAP)
  • Lead Developmemt of the BBMRI-ERIC Negotiator
  • Contribution to the ISO Sample Provenance Standard
  • TEAMING with CY & CZ, Biobanking, Quality- and Sample Management
  • Federated Machine Learning (ML) for Computational Pathology
Principle Investigator (PI)
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