Foundation · Healthcare AI programme · NDA
Spinal cord segmentation under scarce labels
Limited labels, high clinical stakes. A confident wrong segmentation is worse than a slow human read — accuracy without trust criteria is theatre.
Lead outcome
90%+
Dice ~0.9 accuracy at handoff
Focus
What I owned
I kept clinical trust criteria visible in every gate while transfer learning closed the accuracy gap — validation discipline equal to the model work.
Narrative
USA medical imaging client under NDA. The engineering problem was segmentation with thin labels. The programme problem was a buyer who could not afford confident wrongness.
My seat: keeping validation, failure modes, and clinical sign-off on the critical path — not as a week-twelve surprise after a demo glow.
Same operator pattern as every other pillar: absorption and accountability before polish.
Validation equals model work
If the trust criteria are weaker than the network, the programme is not done.
Scarce labels, scarce shortcuts
Transfer learning is a tool. Clinical gates decide whether it ships.
90%+
Dice ~0.9 accuracy at handoff
Transfer
learning under scarce labels
Clinical
trust criteria I held as gates
Next step
Want the confidential layer — names, constraints, steering artefacts? That conversation stays over email.
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