Genentech develops breakthrough labeling process for medical imagery ML
Genentech is a biotechnology enterprise that has developed breakthroughs in medical research and medicine since 1976. Today, Genentech researchers are building convolutional neural networks to help diagnose illnesses and aid medical professionals.
While traditional, classic algorithms can perform when faced with "perfect" data, actual patient data is often riddled with small abnormalities. For example, images of a perfect retina are easily analyzed by a classical algorithm, but a real patient's retina might have multiple lesions, signs of illness, and other issues. Training deep learning algorithms to find and classify images taken from real patients often requires meticulously labeled medical imagery numbering in the hundreds and even thousands.
Typically, only trained medical experts are trusted to correctly annotate training data for these use cases, as inaccurate model predictions can result in misdiagnosis and even loss of life. Labeling the required amount of data this way, however, is a costly and time-consuming endeavor.
Genentech has revolutionized their ML process by having domain experts train teams of labelers on medical imagery annotation tasks. With regular labelers creating annotations, which are then sampled and reviewed for quality by experts, Genentech has developed a much faster and less expensive way to create training data and get their life-saving algorithms to production. Watch the video to learn more about their story.
You can hear from Genentech as well as other enterprises as they discuss their ML journeys and best practices at Labelbox Accelerate 2021, from October 20-21st. Register today to save your spot for this virtual event!