AI service — AI Medical
Digital pathology analysis
Analyse whole-slide images in our GPU clusters — tissue and nucleus classification, region search and slide-level prediction — with an on-premise installation where slides may not leave the lab.
Send us your data volume, throughput and latency targets and any constraint we should design around. You get a proposed configuration, a benchmark on your own data, and a known cost per unit of work before you commit.
A whole-slide image is a gigapixel file, so the work is done in tiles: a pathology foundation model turns each tile into a vector, and those vectors support classification, similarity search and slide-level prediction. What you build on top is small and trains on your own labelled slides in hours, not weeks.
Slide files are large, so transfer is planned rather than assumed: we ingest over SCP behind a VPN, from a NAS or DAS sent to us, or — for labs that cannot move slides at all — from an on-premise installation of the same pipeline. Output is a heat map over the slide, region-level labels or a slide-level score, with the model version recorded for every slide processed.
Every output is clinical decision support, not a diagnosis: a qualified professional reviews and signs it. Where clinical use requires regulatory approval in your jurisdiction, that approval remains yours to hold — we provide the infrastructure, the model operations and the audit trail behind it.
What we size for
We build the digital pathology analysis pipeline around the workload you actually have: data format, accuracy target, latency, throughput, concurrency, retention and scheduling. Start with a pilot, then scale production capacity without changing a line of your integration.
Common use cases
- Region flagging — Mark regions of interest on a slide so a pathologist reviews the relevant fields first.
- Similar-case retrieval — Find previously reported slides that resemble the one on screen.
- Biomarker research — Train a slide-level predictor on your own labelled cohort from existing vectors.
- Quality control — Detect blur, tissue folds and staining variation before a slide reaches a reader.
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