VECTORIO“Your slide model works in a notebook; the hospital cannot deploy a notebook”
Slides classified. Results signed. On-prem.
Deployable on-prem slide classification with per-ROI traceability
- Our slide-analysis code is research Python; hospital IT will not deploy it.
- Gigapixel slides crush our workstation and every slide takes minutes to load.
- I cannot prove which model version scored this slide.
- Deployable on-prem slide classification with per-ROI traceability
- Validate new models without re-engineering the pipeline
From today to the first result.
research Python notebooks
Slide-analysis code stays a research notebook that hospital IT will not deploy, and a gigapixel slide takes minutes to load.
A whole slide of 294,576 rows runs fully local in 46.2 s, and the result is a signed CSV with a hash per tile.
Run one slide through the 3-node pipeline on a lab workstation and compare it with your notebook.
one slideWhole-slide pathology, fully local. Step by step.
A gigapixel slide is read tile by tile, blank glass is skipped, stains are separated and texture features computed — on one machine, without uploading the image.
- Camelyon16 ReaderPyramidal TIFF
- Tissue DetectionTissue Detector
- H&E DeconvolutionColor Deconvolution
- Texture FeaturesFeature Extractor
- Feature CSV ExportCSV Publisher
wsi-read → Image.PyramidalTiff.Subscriber
tissue-detect → Health.TissueDetect
stain-deconv → Health.StainDeconv
features → Classification.Features
csv-out → CSV.Publisher- Camelyon16 ReaderStreams the slide image in 64-tile batches without loading the whole file.
- Tissue DetectionSkips blank glass by thresholding saturation and brightness per tile.
- H&E DeconvolutionSeparates haematoxylin and eosin stains (Ruifrok-Johnston) so structure can be measured.
- Texture FeaturesComputes texture features (GLCM and LBP) per tile.
- Feature CSV ExportWrites the feature table for a classifier or statistician.
A real workflow from our end-to-end test suite; the designer view is illustrated.
What you get. From the first pilot.
Deployable on-prem slide classification with per-ROI traceability
Validate new models without re-engineering the pipeline
Why your tools fall short.
Vendor viewers/AI are closed and per-slide priced
Cloud inference blocked by privacy
Research notebooks lack deployment, signing and audit
- Clinical AI adoption
- GDPR and medical-device scrutiny of AI provenance
- Slide scanners producing growing archives
Your pilot. Three steps.
Your data shape
Tell us what data you have, where it lives and what the result must be.
A scoped pilot
On your data and your hardware, with the success measure written down first.
A clear decision
You see the result against that measure — and keep every workflow and record.
Related roles in Research & geodata.
Bring us your hardest data problem. Let's solve it together.
A short pilot on your data and your hardware. We agree on what success looks like before we start.