Data labeling for medical images

AI companies are increasingly using medical datasets from digital photography. This technology is ubiquitous and easy to use, with the potential to break through geographical obstacles to care.

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We’ve worked with clients to structure image data through our annotation services — both classifying these images and segmenting regions of interest. Examples include classification of skin lesions and segmenting retinal slit lamp videos.

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Highlighted case study

Case study

Brigham and Women's Hospital engaged Centaur Labs to automate the prediction of COVID-19 disease status from point-of-care ultrasound

Research teams at Brigham and Women's Hospital engaged Centaur Labs in their goal to automate prediction of COVID-19 disease status from point of care ultrasound, funded by a grant from the Massachusetts Life Sciences Center.

With mobile-friendly, gamified labeling tools, the Brigham’s experts labeled thousands of ultrasound stills. Centaur is working closely with the Brigham’s machine learning engineering team to refine the model, which is still in development.

"Centaur's mobile-first labeling tools enable us to quickly and conveniently gather results from our residents. We've accelerated our research by building and refining our ML models on COVID-19 pathologies in near real time. Centaur Labs has been a crucial partner throughout."

Andrew J. Goldsmith, MD, MBA
Andrew J. Goldsmith, MD, MBA
Director of Emergency Ultrasound, Emergency Medicine
Brigham & Women's Hospital

Case study

Auggi's gut health app transforms the lives of patients living with chronic gastrointestinal conditions

Centaur Labs classified 15,000 user-submitted stool images captured by their smartphones into Bristol stool scores. Centaur's data labels improved Auggi's model to an overall accuracy of 94%.

Case study

ISIC and Centaur Labs designed a medical image classification task for ISIC’s archive of over 11,000 dermoscopic skin lesion images

Across three labeling tasks including malignancy classification and lesion recognition, Centaur collected over half a million opinions from its network of experts. Recent research published in the Lancet assessed how accurate board-certified experts with 10+ years experience were at this multi-class skin-lesion labeling task. The performance of the Centaur’s network without any experience exclusion criteria was more accurate (78.1%, p<0.001, vs 74.7%) than those physicians.

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