RequestUpdated on 12 January 2026
Multi-centric validation of AI models for prostate cancer screening
Data strategist at BBMRI-ERIC EOSC Node
Vienna, Austria
About
Overview
The BBMRI-ERIC EOSC Node invites organizations to join the Multi-Centric Validation of AI Models for Prostate Cancer Screening (MCVAL). MCVAL aims to validate prostate cancer screening AI models across diverse datasets and secure computing environments.
Who Can Contribute?
Hospitals and pathology departments; Biobanks and research infrastructures; HPC/Cloud providers capable of secure data processing; AI developers in digital pathology.
A) Sensitive Health Data
Contribute WSIs and metadata meeting specifications: prostate biopsy WSIs, OpenSlide-readable formats, slide-level labels, optional spatial annotations, GDPR-compliant data handling, informed consent.
B) Secure Computational Resources
Provide infrastructure for sensitive data: ISO 27001-class security, SPE principles, GPU-enabled HPC/cloud, container orchestration.
C) AI Models for Prostate Cancer Screening
Contribute AI models for WSI analysis, including slide-level or patch-level classifiers, weakly supervised or foundation models, container-deployable formats.
Benefits of Participation
Advance early detection; increase visibility within EOSC; strengthen reproducibility; contribute to secure European health-data research.
Organisation
Similar opportunities
Service
- VRE
- Other
- Federated AAI
- Federated Compute & Storage
- Scientific workflows and services
- Integrating scientific data repositories
Nils Hoffmann
PI Datascience and Bioinformatics for Mass Spectrometry at Forschungszentrum Juelich GmbH
Bielefeld, Germany
Project cooperation
HORIZON-INFRA-2026-01-EOSC-02: Trusted frameworks for secure and efficient data sharing in EOSC
Tomasz Miksa
OSTrails
Wien, Austria
Service
- Federated Compute & Storage
- Scientific workflows and services
- Service Catalogues, Interoperability, & Integration
Enol Fernández
Principal Software Architect at EOSC Data Commons
Amsterdam, Netherlands