ExaMode: Extreme-scale Analytics via Multimodal Ontology Discovery & Enhancement
Exascale volumes of diverse data from distributed sources are continuously produced. Healthcare data stand out in the size produced (production is expected to be over 2000 exabytes in 2020), heterogeneity (many media, acquisition methods), included knowledge (e.g. diagnosis) and commercial value. The supervised nature of deep learning models requires large labeled, annotated data, which precludes models to extract knowledge and value. Examode solves this by allowing easy & fast, weakly supervised knowledge discovery of exascale heterogeneous data, limiting human interaction.
Publications
Project members
- HES-SO
- University of Padova
- Ontotext
- Radboud University
- Microscopeit
- Cannizzaro Hospital
- SurfSara
Team members
- Henning Müller (HEAD)
- Manfredo Atzori (HEAD)
- Sebastian Otálora
- Niccolo Marini
Acknowledgements
This project has received funding from the european Union’s Horizon 2020 research and innovation programme under grant agreement no. 825292