Project description
Application of extreme analytic methods in healthcare
Exascale volumes of diverse healthcare data stand out in size (the 2020 production exceeded 2 000 exabytes), heterogeneity (numerous media and acquisition methods), knowledge (diagnostic reports) and commercial value. The supervised nature of deep learning models requires large labelled, annotated data, preventing models from extracting knowledge and value. The aim of the EU-funded EXA MODE project is to allow easy and fast, weakly supervised knowledge discovery of exascale heterogeneous data, limiting human interaction. The project objectives include the development and release of new methods and tools for extreme-scale analytics for precise predictions, supporting decision making by industry and hospitals. The multimodal semantic middleware will offer easier and faster management and analysis of heterogeneous data, improving architectures for complex distributed systems and increasing the speed of data throughput and access.
Fields of science
Programme(s)
Funding Scheme
RIA - Research and Innovation actionCoordinator
2800 Delemont
Switzerland
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Participants (9)
35122 Padova
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1784 Sofia
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6525 XZ Nijmegen
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53-603 Wroclaw
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
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95126 Catania
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1098 XG Amsterdam
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1784 Sofia
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6525 GA Nijmegen
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95124 Catania
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