Computer vision · Medical
Medical imaging & diagnostic support
Segmentation, measurement, and decision support built with research-grade rigor.
Prototype in 8–12 weeks
The problem
Medical and veterinary imaging AI fails for predictable reasons: models that don't generalize across equipment, evaluation that ignores inter-observer variability, and products built without a path through validation and regulation.
Getting it right requires both deep learning expertise and an understanding of how clinical measurement, uncertainty, and review actually work.
Our approach
Clinical task definition
With your specialists: what is measured, what decision it supports, and what accuracy clinicians need to trust it.
Data strategy with variability in mind
Multi-device, multi-operator data handling; annotation protocols that capture expert disagreement instead of hiding it.
Model development and uncertainty
Segmentation, measurement, and classification with calibrated confidence — flagging cases that need human eyes.
Rigorous evaluation
Held-out evaluation designed like a study, not a demo: per-cohort metrics, failure analysis, comparison to inter-observer baselines.
Prototype and integration path
API and interface prototypes for clinical workflows, and an honest map of the validation and regulatory road ahead.
Reference architecture
- DICOM/image ingestion and anonymization
- Segmentation and measurement models with uncertainty estimates
- Clinical metrics extraction and structured reporting
- Review interface for specialist verification
- API for integration with PACS or practice software
What you get
- A working prototype evaluated with study-grade rigor
- Clear metrics against expert performance, per cohort
- An honest assessment of the path to clinical deployment
- Documentation designed to support later validation work
Proof. Led by a PhD in AI for medical imaging, with peer-reviewed research in Computer Methods and Programs in Biomedicine and Biomedical Signal Processing and Control, and diagnostic-support systems built for medtech clients including Microview.ai.
Build imaging AI with research-grade rigor.
A 30-minute call is enough to tell you whether this is feasible on your data.