Data infrastructure.
Organize and query multi-modal imaging, clinical context and technical metadata at study level.
Buyer-specified medical imaging
Medical imaging datasets built to your exact specification
Define the modality, pathology, cohort, ground truth, geography and permitted use. MedCorpora assesses matching hospital inventory and sourcing options before any patient-level data moves.
Beyond the benchmark
Clinical archives are not benchmark datasets
Missing sequences. Scanner drift. Inconsistent DICOM. Labels trapped in reports. The gap between a promising model and reliable clinical performance is not only a model problem. It is a data infrastructure problem.
Programme operations.
One control plane for hospital inventory, permissions, privacy, quality, provenance and release state.
Model evidence.
Materialize versioned cohorts for training, evaluation and site, scanner and population holdouts.
Pipeline.
De-identify, resolve, normalize, enrich, validate, version and deliver under controlled terms.
$ medcorpora release --programme clinical-imagingMedical models learn reality from variation across hospitals, scanners, protocols and populations. The bottleneck is not only compute. It is authorized, organized and clinically defensible data.
Explore the data engineEnterprise engagementBook technical scoping
Start with a defensible feasibility brief.
Define the clinical task, target population, imaging specification, evidence standard and permitted use before any patient-level data moves.
Hospital authorization and permitted-use scope.
De-identification method and technical tag policy.
Case, sequence, scanner, label and QC distributions.
Sample review, exclusions, release terms and lineage.
Direct answer
Hospital data infrastructure for medical AI developers.
MedCorpora is a specialized data infrastructure and operational management platform connecting participating hospital archives to governed medical AI datasets. It coordinates inventory, written programme authority, de-identification, DICOM and clinical-data engineering, quality evidence, licensing and controlled release.
Read the complete capability brief ↗Company and sourcing
Clear provenance before data moves.
MedCorpora is based in Bangalore, Karnataka. Current sourcing availability includes hospital relationships in India; US and other geographic requests are assessed through buyer-specific feasibility.
- Operating base
- Bangalore, Karnataka, India
- Source network
- Participating hospitals and hospital networks in India
- Source control
- Hospitals retain source records and any re-identification mapping
- Release authority
- Programme-specific authorization established before an approved release
Programme status
Brain MRI Study-level verification and privacy clearance
Liver MRI + MRE Sourcing scope
Non-contrast CT Sourcing scope
Review the rights model ↗The MedCorpora standard
Enterprise data is bought on evidence, not volume.
Clinical data becomes infrastructure when hospital authority, privacy controls, technical completeness, ground truth and provenance are connected in one governed release.
PermissionPrivacyStructureClinical truthQualityProvenance
MedCorpora Data Engine
Hospital data becomes programmable infrastructure.
A modular execution layer runs governed Python transformations, imaging intelligence and quality controls across clinical data. Every output stays connected to its source, policy and release history.
Explore the platform ↗Connect
Controlled ingestion from hospital PACS, archives and approved clinical exports.
Resolve
Patient-copy, study, series and instance relationships reconstructed into a traceable data graph.
Govern
Policy, permitted use, privacy controls and technical allow-lists are enforced at execution time.
Transform
Versioned Python jobs perform de-identification, conversion, normalization and metadata engineering.
Interpret
Task-specific models classify series, detect anomalies and stage clinical enrichment for review.
Train
Reproducible cohort compilation, patient-safe splits, experiment runs and model artifact tracking.
Validate
Performance, calibration, subgroup and external-cohort evidence are attached to each model release.
Release
Immutable manifests, lineage, access controls and delivery records govern data and model products.
Hospital systemsGoverned data graphTraining + evaluationControlled releases
Data programmes
Buyer-defined programmes. One operating standard.
Programmes begin with the clinical and technical requirement, then map it against verified hospital inventory. The scopes below are reference programmes, not the limits of the platform.
Brain MRI
Hospital-sourced, multi-vendor neuroimaging data engineered for model development and validation.
Open programme brief ↗Sequences
3D T1 · T2 · FLAIR · DWI / ADC · selected perfusion
Acquisition
1.5T and 3T · multi-vendor
Data
Source DICOM · derived NIfTI · metadata · QC
Clinical layer
Reports, diagnosis and task-specific annotations where verified
Liver MRI + MRE
Hospital-sourced liver imaging across disease, scanner and population distributions.
Open programme brief ↗Sequences
MRE · T1-weighted · T2-weighted · diffusion
Pathology
MASH / MASLD · HBV · HCV · cirrhosis · AIH · PSC
Cohort
Defined disease labels · demographics · multi-scanner coverage
Scale
Pilot to hundreds of studies, subject to verified inventory
Non-contrast CT
Hospital CT cohorts structured across cardiovascular, pulmonary, bone and body-composition applications.
Open programme brief ↗Applications
Cardiovascular · lung · bone · body composition
Acquisition
Scanner model · reconstruction · slice thickness · protocol
Ground truth
Reports · quantitative measurements · clinical outcomes
Validation
Site, scanner and demographic holdout design
Dataset package
What constitutes a release.
Every release is assembled against a defined clinical programme. Imaging, clinical context and annotations are included only when verified.
Source imaging
Study and series-level DICOM organized without losing clinically relevant acquisition context.
Derived data
NIfTI volumes, normalized orientation, series mapping, thumbnails and machine-readable metadata.
Clinical context
Radiology reports, disease labels, measurements, segmentations or outcomes when included in scope.
Quality evidence
Completeness checks, exclusions, acquisition distributions, annotation review and a dataset card.
Rights package
Hospital authorization, permitted-use terms, de-identification documentation and supplier records.
Programme architecture
The dataset control plane.
A common operating model connects hospital inventory, clinical scope, technical structure, evidence, governance and access.
Clinical target
Anatomy · pathology · model task · intended claim
Imaging
Modality · sequences · contrast · raw or derived data
Acquisition
Vendor · model · field strength · protocol · reconstruction
Cohort
Case count · controls · disease mix · demographics · geography
Ground truth
Reports · labels · masks · measurements · outcomes · reviewer standard
Quality
Completeness · artefacts · exclusions · acceptance criteria
Commercial
Permitted use · pilot and final volume · timeline · exclusivity
Supplier readiness
Built for diligence, not just demos.
Enterprise diligence verifies the supplier, hospital authority, population distribution, de-identification method and release history.
01Corporate verification
02Hospital authorization
03Privacy documentation
04Population analysis
05Technical data card
06Acceptance criteria
Operating model
Specification to secure delivery.
Programme inventory is established before release. Data moves only after rights, privacy scope and technical controls are defined.
Define
Convert the model roadmap into a testable data specification.
Inventory
Check aggregate hospital inventory before requesting exports.
Authorize
Confirm hospital rights, commercial scope and governance.
De-identify
Remove identifiers while preserving an approved technical tag allow-list.
Curate
Classify series, derive volumes, join labels and document QC.
Validate
Review a small sample against technical and clinical acceptance criteria.
Licence
Execute supplier terms, permitted use and secure delivery.
Institutional engagement
Define the data before moving it.
Bring the clinical task, modality and intended use. We will turn them into a hospital-ready feasibility specification.