The dataset release becomes an executable contract
Training does not begin from an arbitrary folder. A release manifest defines the eligible studies, labels, exclusions, allowed transformations and intended split strategy. The pipeline compiles that manifest into deterministic train, validation and test materializations without crossing patient or programme boundaries.
Versioned model-development graph
Preprocessing, augmentation, sampling, optimization, checkpoints and evaluation execute as a traceable graph. Dataset version, code version, configuration, model artifact and metric output remain connected so an experiment can be reproduced or invalidated when its source data changes.
- Patient and longitudinal leakage prevention
- Modality-aware normalization and augmentation policies
- Class, site, scanner and demographic sampling controls
- Experiment configuration, checkpoint and model registry lineage
- Calibration, thresholding and failure-mode analysis
- Subgroup, hospital, scanner and external-cohort evaluation
- Promotion gates for data and model releases
Training remains inside policy boundaries
The execution layer does not expose hospital data to arbitrary jobs. Training workloads reference approved releases, inherit permitted-use and retention controls and emit only authorized artifacts. Exceptions, failed gates and prohibited outputs remain visible in the programme record.
Questions, answered directly.
Can the same pipeline train different architectures?+
Yes. The orchestration layer can parameterize task-specific architectures while preserving a common dataset, policy and evaluation record.
How is leakage prevented?+
Split compilation operates on pseudonymous patient and longitudinal relationships rather than individual files or slices.
Does a strong aggregate metric complete validation?+
No. Medical AI evidence should also examine calibration, subgroup performance, site and scanner behavior and clinically important failure modes.