GrandQC
Tissue detection and multi-class artefact segmentation for whole slides.
Run this before trusting any result computed on a batch of scanned slides. Most 'model failure' turns out to be slide quality.
- Cost
- Free
- Hardware
- Needs a consumer GPU
- Offline
- Works without a connection
- Your data
- Stays on your machine
- Licence
- unknown
- Maturity
- production
- From
- University Hospital Cologne
What it is
Segments tissue and the common artefacts that ruin downstream analysis — tissue folds, pen marks, bubbles, edges, black spots, foreign objects and out-of-focus regions. Released with a manually annotated test set and QC masks for all of TCGA.
Licence notes
Check the repository before any reuse.
Activity: 113 stars · last commit 2025-12-27 — refreshed nightly
Related
Catalogued 2026-08-02, last checked 2026-08-02. View the source record · Report an error