SANGHA Research Systems
Project / 01
Pre-release · 2026
Computational pathology / reproducible measurement

Adaptive Whole-Tissue Measurement for QuPath

SANGHA Research Systems × Cherie Stringer, PhD

A reproducible exact/adaptive whole-tissue measurement system for QuPath pixel classifiers, developed from a production pathology workflow and generalized into a parallel, resumable measurement architecture with statistically controlled early termination.

Exact kernel · promoted Runtime parity · validation pending Adaptive estimator · gated
Reference

Whole-tissue exact measurement

The validated reference workflow exhaustively partitions annotated tissue, measures classifier-positive and classifier-negative area, and pools those measurements into a whole-tissue result.

Systems

Parallel, resumable execution

QuPath remains the measurement kernel. Nextflow owns image-level dispatch, isolation, retry and provenance; Make exposes simple human-facing verbs for exact measurement, estimation, verification and resume.

Inference

Adaptive stopping, after parity

The adaptive path will use spatially balanced sampling with a finite-population ratio estimator and LLN/CLT-derived precision criteria. It remains disabled until exact numerical parity is demonstrated.

Release gate
01 / PromoteReference kernel behind the shared workflow API.
02 / ParitySame slide, classifier and annotations; same scientific result.
03 / AdaptSpatially balanced anytime estimator and stopping law.
04 / ReleaseVersioned QuPath-facing package, tests and provenance.

Current status. The exhaustive kernel has been promoted into the new workflow surface. The next hard gate is runtime parity against the original production implementation. Adaptive stationary estimation is intentionally unavailable until that comparison passes.

Code / GitHub →
Cherie Stringer, PhD / @StringerCA →