With the exponential growth of clinical data, and the fast development of AI technologies, researchers are facing unprecedented challenges in managing data storage, scalable processing, and analysis capabilities for heterogeneous multisourced datasets. Beyond the complexity of executing data-intensive workflows over large-scale distributed data, the reproducibility of computed results is of paramount importance to validate scientific discoveries. In this paper, we present MULTIX, a cross-domain research-oriented platform, designed for collaborative and reproducible science. This cloud-based framework simplifies the logistical challenges of implementing data analytics and AI solutions by providing pre-configured environments with ad-hoc scalable computing resources and secure distributed storage, to efficiently build, test, share and reproduce scientific pipelines. An exemplary use-case in the area of cardiac image analysis will be presented together with the practical application of the platform for the analysis of ~20.000 subjects of the UK-Biobank database.

Original document

The different versions of the original document can be found in:

Back to Top

Document information

Published on 01/01/2019

Volume 2019, 2019
DOI: 10.1109/bibm.2018.8621317
Licence: CC BY-NC-SA license

Document Score


Views 1
Recommendations 0

Share this document


claim authorship

Are you one of the authors of this document?