Implementation of an interoperable pan-European federated repository of health images, including secure data sharing mechanisms

An Interoperable pan-European federated repository of health images that allows sharing data in compliance with legal, ethical, privacy and security requirements, for AI-related training and experimentation; the repository will rely on federated data storage and will operated on a Federated Learning basis, abiding to the highest data privacy and security standards. It will also offer High Performance Computing-as-a-service, where necessary, thereby allowing forfor cost-effective performance of computationally intensive processing without the need for maintaining expensive equipment. Towards this direction the following objectives will be implemented:

Objective 3.1
To incorporate a federated data storage schema: enabling the data providers to keep full control of their data, ensure their autonomy and at the same time contribute to the platform.
Objective 3.2
To implement a secure data exchange/sharing and privacy mechanism: based on blockchain technology and eIDAS, enabling secure access to data and identity management and ensuring trust.
Objective 3.3
To develop an automated data curation and an ML-based annotation service: ensuring high- data quality, correct interpretation of data/results, increasing speed and efficiency of annotation by minimizing the human intervention.
Objective 3.4
To incorporate a transaction tracking mechanism: based on blockchain technology recording the INCISIVE data transactions, ensuring transparency and traceability.
Objective 3.5
To define and implement a data donorship mechanism: following all related legal directives and enabling users to share their data while keeping full control of it.
Objective 3.6
To incorporate a data anonymization mechanism: enabling data processing, with respect to all related legal and ethical aspects. The mechanism will be bi-directional, including also a de-anonymization process.
Objective 3.7
To implement an interoperable data integration service: based on a common data model and well-established standards (HL7 FHIR, DICOM, SNOMED), enabling data providers to easily link their data sources to the INCISIVE platform.