Medical imaging annotation by specialist clinicians.
Imaging models are only as good as the labels behind them. We match your images to clinicians with the right specialty, design a review that fits the task and back it with analysis from PhD data scientists.
The result is ground truth you can defend, whether you are training a model or testing one.
The right specialist for every image.
Our reviewers come from the BiteLabs network of more than 30,000 clinicians and healthcare professionals. We check professional registration and experience, and confirm the right expertise for your project during scoping.
Matched to how hard the task really is.
Single expert read
One appropriately qualified reviewer per image, with quality checks on a sample. Suits clear-cut tasks and early prototypes.
Independent double read
Two reviewers label each image without seeing each other's work. Where they disagree, a senior reviewer adjudicates.
Agreement analysis
PhD data scientists measure how often reviewers agree, so you know how reliable the labels are and where the task is genuinely hard.
Decisions we settle with you.
Most labelling problems start before the first image is labelled. We agree these with you during scoping, then document the criteria so the work can be repeated.
- Which specialty and level of seniority does the task need?
- Is one read enough, or do you need independent reads and adjudication?
- What is the reference standard: reader consensus, or a confirmed outcome where one exists?
- How should borderline and ungradable images be handled?
- What output does your pipeline need?
WHAT YOU RECEIVE · DEPENDING ON SCOPE
- Annotated datasets
- Clinically reviewed labels
- Structured reference data
- Quality assurance findings
- Reviewer agreement analysis where appropriate
- Documented annotation criteria
- Outputs compatible with agreed workflows
Often combined with.
Let's solve your next clinical AI challenge.
Tell us what you're developing and we'll reply the same working day.