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A working research pipeline

Not a demo — the reproducible harness every experiment runs through.

The same infrastructure that serves our chest-imaging consensus is the harness we validate hypotheses in. It de-identifies data, runs adapted open-weight models, scores them leave-one-site-out, and returns calibrated, human-in-the-loop results — so collaborators get a validated answer without building any of it.

Architecture

Always-on CPU, bursting to GPU only when it counts.

A cheap, always-on control plane does ingestion, de-identification, gating, consensus, calibration and validation. Heavy models run on an on-demand GPU that scales to zero between studies — research-grade capability without a standing data-centre bill.

Data in images · reads · markers Always-on CPU control plane De-identify + canonicalize Gate / QC abstain Readers & probes Validate + calibrate Calibrated draft + metrics On-demand GPU scale-to-zero · seconds / study
How it helps research

Bring a hypothesis and data. Skip the plumbing.

The hard, unglamorous parts of a rigorous study are already built and tested. Collaborators plug in and get straight to the science.

De-identification first

PHI is stripped and inputs canonicalized before anything is scored — so data-sharing and the removal of site shortcuts happen in one step.

Leave-one-site-out, baked in

The harness scores on held-out sites and cohorts by default. You get the number that matters — external performance — not the one that flatters.

Calibration & abstention

Outputs are calibrated per site and the pipeline abstains on out-of-scope inputs — results a clinician can actually act on, with uncertainty made explicit.

Reproducible & version-pinned

Every stage is deterministic and pinned, so an experiment re-runs to the same answer — and a reviewer can trace exactly what produced it.

Standards-based interop

DICOM in and out (segmentation, structured reports, C-STORE) for imaging; version-pinned, auditable stages for genomic and marker data.

Frugal by design

CPU-first with scale-to-zero GPU keeps a full study cheap — so cross-site research isn't gated by a data-centre budget.

Working with us

From question to validated result.

01

Frame the hypothesis

Together we state a falsifiable claim and the metric that decides it.

02

Share data securely

De-identified at ingest; you keep control, we remove site shortcuts.

03

Run & validate

Adapt open-weight models; score leave-one-site-out; calibrate.

04

Keep or reject

A clear verdict — proven, rejected, or open — and a reproducible record.

Propose a study →

Have a question the plumbing keeps getting in the way of?

That's exactly what this pipeline is for. Bring the hypothesis; we'll bring the harness.