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Centaur Labs raises $15 million, led by Matrix Partners, to label the world’s medical data, accelerate AI development

Press releases may be edited for formatting or style | September 07, 2021 Artificial Intelligence Business Affairs
BOSTON--(BUSINESS WIRE)--Centaur Labs, a medical data labeling company, today announced $15 million in funding to advance their mission to label the world’s medical data. The Series A round was led by Matrix Partners with participation from other funds including Accel, Global Founders Capital, Susa Ventures, Y Combinator, and individual investors including John Capodilupo (founder and CTO of WHOOP), Tom Lee (founder of One Medical), and Elliot Cohen (founder and CPO of PillPack). The new capital will fund the expansion of the company’s global network of labelers and accelerate product development and hiring.

Artificial intelligence is enabling extraordinary advances in healthcare, with the potential to reduce costs and drastically improve healthcare outcomes. However, AI is only as accurate as the data it is trained on. While nearly 30% of the world’s data is generated by the healthcare industry, it is largely unstructured and poorly labeled. To train AI algorithms, healthcare companies require massive labeled datasets of medical images, videos, text or audio recordings, and the efficacy of these algorithms directly depends on the accuracy of the underlying data labels.

To tackle this problem, Centaur has cultivated a network of tens of thousands of medical students and professionals from over 140 countries. This network primarily labels data on Centaur’s gamified iOS app, DiagnosUs, where labelers improve their skills and compete with one another. The app is designed to judge labelers on their performance and reward the most accurate labelers with cash prizes. Importantly, Centaur collects multiple opinions on every case—with more opinions collected on the most difficult cases—and intelligently combines those opinions into labels that are more accurate than those from an individual expert. More than 1 million opinions are contributed through the platform each week.

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“AI learns like humans—by example—and to train an algorithm it takes thousands or even millions of examples. It is difficult to curate large medical datasets, and nearly impossible to source accurate labels from those with medical knowledge and specialized training,” said Erik Duhaime, co-founder and CEO of Centaur Labs. “Our platform is built to support a wide range of specialized medical tasks, and to quickly scale to millions of labels.”

Grand View Research reports that the global data annotation tools market size is expected to reach $1.6 billion by 2025. Centaur provides annotations for leading medical AI startups like Eko Health, which uses recordings of heart and lung sounds to train AI algorithms for their stethoscope technology, and for researchers at medical institutions such as Brigham and Women’s Hospital, which is leveraging ultrasound data for new AI applications.

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