Clinical Imaging Infrastructure • Pathology Imaging

Pathology AI needs slide‑level traceability and patch‑level QC — or it won’t be trusted.

Whole-slide imaging breaks silently with stain shifts, scanner differences, patch sampling bias, and subjective ground truth. Trinzz treats pathology datasets as governed systems: WSI provenance, tile sampling governance, patch QC, quantified consensus, and evidence packs shipped with every versioned release.

SOC 2 Type II ISO 27001:2022 HIPAA compliant PHI controls + audit logs Versioned releases

Imaging annotation — engineered for deployment

Trinzz turns imaging annotation into a governed reliability system: AI assists, experts validate, agreement is quantified, QC is systematic, and releases ship with evidence.

Clinical‑grade ground truthExpert validation + adjudication + consensus confidence — per case.
Reliability scoring (CG‑DRS)Release gates backed by quality, traceability, and drift readiness metrics.
Evidence PackQC report, consensus report, lineage export, monitoring spec.
Monitoring loopDrift triggers + edge‑case loopback to continuously improve.
Clinical Imaging Infrastructure • Pathology Imaging annotation lifecycle diagram
Clinical‑grade lifecycle: governed sampling + expert consensus + patch QC + release gate.
WSI governance stack

Whole‑slide datasets that reviewers can defend

This is what turns a dataset into something procurement + clinical validation can sign off on.

TraceabilityConsensusQCMonitoring
WSI Provenance GraphSlide lineage, scanner/stain metadata, preprocessing versions captured as evidence.
Tile Sampling GovernanceDocumented sampling plan to prevent patch bias and ensure reproducibility.
Patch/Tile QCFocus/artifact checks, stain shift indicators, distribution sanity checks.
Slide-Level ConsensusExpert agreement on grading/regions with dispute routing + confidence.
Shift Readiness (Stains/Scanners)Baseline vs current drift thresholds; triggers for recalibration.
Release Gates + Evidence PackCommittee-ready exports: QC, consensus, lineage, monitoring spec.
WSI governance

Sampling, stain shift, and patch QC — made explicit

Pathology datasets break when sampling and staining are implicit. We turn them into governed artifacts.

Sampling planStain driftFocus/artifacts

Sampling plan as evidence

How tiles are chosen, excluded, stratified, and re-sampled—so training is reproducible and defensible.

  • Inclusion/exclusion criteria
  • Hard‑negative strategy
  • Slide/region stratification

Patch QC that catches silent failure

Automated checks for focus, artifacts, and stain intensity shifts—before they become model regressions.

  • Focus score thresholds
  • Artifact detection flags
  • Stain shift indicators + triggers
Use cases

Where this should convert

Written for enterprise imaging teams evaluating deployment readiness.

HospitalsDiagnostics networksMed‑AI enterprises
Tumor region segmentation & gradingExpert-validated regions + consensus confidence for ambiguous morphology.
Foundation model tile librariesLarge-scale patch corpora with strict provenance and QC.
Multi-lab generalizationQuantify stain/scanner/site gaps and plan targeted remediation.
Clinical trial dataset programsVersioned releases, change control, and evidence packs.
Vendor evaluationBenchmark dataset reliability before model selection or deployment.
Ongoing monitoringStain/scanner drift alerts + loopback into validation.
Evidence pack

What you get with every release

Evidence reduces “trust debates” to artifacts.

QC reportConsensus reportLineage exportMonitoring spec
  • WSI slide + tile manifests + version hash
  • Patch QC report: focus/artifact rates, stain shift indicators, bias checks
  • Consensus report: agreement metrics + adjudication summaries
  • Sampling governance memo: inclusion/exclusion criteria + SOP mapping
  • Monitoring spec: drift alerts + escalation workflow back into validation
  • Lineage export: provenance, reviewer identity, timestamps, SOP references
Release Gate: Pass / Conditional / Fail based on QC strength, consensus confidence, and traceability completeness.
Reliability is not a claim. It's a tracked system — with thresholds, evidence, and release discipline.
Request a Reliability Audit Share a dataset manifest. We return CG‑DRS diagnostic + validation plan + monitoring triggers.