Programmable, but governed
Hospital data requires flexibility because every archive, modality and clinical programme is different. The platform executes configurable pipelines without turning governance into an afterthought. Each job runs against an approved scope, records its inputs and outputs and writes evidence into the release manifest.
One control plane across modalities
Imaging workflows share a common operating layer even when their task modules differ. Brain MRI series classification, liver MRE map handling and CT reconstruction checks can run as separate modules while identity policy, lineage, quality reporting and release controls remain consistent.
- PACS and archive ingestion with study-level inventory
- DICOM tag policy, pixel privacy checks and pseudonymous identifiers
- Series and protocol classification with review queues
- DICOM-to-NIfTI conversion and derived metadata products
- Report structuring, label staging and annotation integration
- Automated quality gates, exception handling and release manifests
- Patient-safe cohort materialization, model training and subgroup evaluation
Designed for repeatable programmes
A successful pipeline is promoted into a versioned programme template. New hospital sources can be evaluated against the same technical and clinical controls while site-specific transformations remain isolated and auditable.
Questions, answered directly.
Does the platform support model training?+
Yes. Governed dataset releases can feed versioned training and evaluation workflows with patient-safe splits, experiment records and model lineage. The exact training scope is defined by programme.
Can custom Python modules be added?+
Yes. Task-specific modules can be introduced under version control, testing, policy and release review.
Is every step fully automated?+
No. Automation handles repeatable transformations and quality checks; privacy exceptions, ambiguous series and clinical labels can require expert review.