Researcher’s View: Lead a Project & Conduct an AI Challenge
CENTAURON empowers clinical and academic researchers to lead multi-institutional AI studies without the barriers of traditional data-sharing agreements. As a researcher, you define the scientific question, coordinate collaborators and oversee the creation of a high-quality cohort — while all data stays at the originating institution.
What is a Project?
A Project is a medically driven coordination layer where the research leader defines:
- Clinical question & study hypothesis
- Inclusion/exclusion criteria
- Required stains (e.g., H&E, CD3/CD8, p16/Ki-67)
- Annotation schema / ground-truth definitions
- Metadata structure & QC requirements
- Ethical and clinical context
The Project acts as the scientific container that brings institutions together and creates a shared vocabulary and data standard — without moving data. It is the digital equivalent of forming a consortium, but open, cryptographically trusted and globally scalable.
What is an AI Challenge?
Once the Project is defined, the researcher can launch an AI Challenge on the same topic. Models are submitted by participants and executed directly on institutional data inside local secure environments. You set:
- Evaluation metrics
- Participation criteria
- Timeline & publication strategy
- Rules for algorithm submission
No images or labels ever leave the institution.
Only performance metrics return to the Challenge lead.
Why Lead a Project in CENTAURON?
| Classic Academic Study | CENTAURON Project |
|---|---|
| Months of DUA negotiation per institution | Instant participation once identity verified |
| Data transfer & anonymization burden | Data stays local — no transfer |
| Difficult to scale beyond 2-3 centers | Designed for multi-center scaling |
| One-off consortium effort | Reusable, persistent collaboration structure |
| Limited external validation | Built-in blinded multi-site validation |
Benefits for Researchers
Coordinate real clinical data across multiple centers Achieve external validation and clinical robustness Maintain scientific leadership and authorship Use encrypted, audit-proof collaboration — future-proof under AI regulation Create durable research networks that outlast single studies Accelerate translation into clinical practice
Typical Projects
Predicting therapy response in oncology Outcome prediction from WSIs and immunostains Multi-site stain harmonization research Foundation model benchmarking in pathology Rare-disease cohort building Explainability and robustness analysis
TL;DR
CENTAURON lets researchers form multi-center research collaborations and run blinded AI validation without moving data. You lead the clinical question and cohort definition — the network provides secure execution and global scalability.