AI Microscopy & Image Analysis
Compare segmentation, counting and classification tools using images representative of your instruments, specimens and acquisition settings.
Technical evaluation & safeguards
- Separate training and evaluation data by specimen or batch to prevent leakage; evaluate relevant false-positive and false-negative errors.
- Annotator agreement, resolution, staining, illumination and acquisition changes affect model performance.
- Retain raw images, labels, software/model version and review decisions; monitor drift after changes.
Prepare a buying or implementation brief
- Image modality, acquisition metadata and biological/material endpoints
- Representative labelled data, rights to use it and review expertise
- Independent validation set, uncertainty and manual-review workflow
A controlled supplier pilot
- Use authorized, de-identified data and a written intended use; do not send client or participant data without appropriate permission.
- Request a demonstration against your own acceptance criteria, including error cases and export/exit requirements.
- Agree integration, security, validation ownership, ongoing monitoring and model-update responsibilities.
- Laboratory Vortex coordinates inquiries, does not endorse products, validate your system or guarantee performance.