Genentech develops breakthrough labeling process for medical imagery ML
Genentech's clinical development team needed advanced quality workflows and secure infrastructure to label medical imagery training data at scale, but relying solely on trained medical experts for annotation was costly and time-consuming, limiting their ability to deploy life-saving AI algorithms.
By having domain experts train labelers instead of performing all annotation themselves, Genentech is now producing labeled data that is 10x cheaper, 5x faster, and with better quality, enabling scaled training data operations and faster algorithm deployment.
Frequently asked questions
What did this team achieve with this AI workflow?
By having domain experts train labelers instead of performing all annotation themselves, Genentech is now producing labeled data that is 10x cheaper, 5x faster, and with better quality, enabling scaled training data o…
What tools did this team use?
Labelbox, convolutional neural networks.
What results were reported?
Labeled data cost: 10x cheaper; Labeled data production speed: 5x faster; Labeled data quality: better quality (source-reported, not independently verified).
How is this quality assurance AI workflow structured?
AI initiative triggers labeling need → Expert-guided labeler training → Labeler annotation creation → Expert quality sampling and review → Labeled training data production.