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ExaMode

Idea:
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.

Objectives:
1. Weakly-supervised knowledge discovery for exascale medical data.
2. Develop extreme scale analytic tools for heterogeneous exascale multimodal and multimedia data.
3. Healthcare & industry decision-making adoption of extreme-scale analysis and prediction tools.

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