Centaur Labs’ latest study tackles human bias in crowdsourced AI training data using cognitive-inspired data engineering. By applying recalibration techniques, they improved medical image classification accuracy significantly. This approach enhances AI reliability in healthcare and beyond, reducing bias and improving efficiency in machine learning model training.
The new AI-powered scientific search engine, Consensus has officially announced its partnership with Centaur Labs to generate high-quality and scalable scientific data labels.
From SMS to insurance claims to pathology reports and scientific studies, in this post we dig into the most common type of medical text datasets leveraged for NLP in healthcare.
In this new era of hybrid work, it's more important than ever to be creative and thoughtful about bringing your team together. Learn a little more about what we're doing to help our team thrive, wherever they're working.
Learn about our partnership with Mayo Clinic spin out Lucem Health, and how clinical AI development teams can access high quality medical data annotations at scale.
Learn more about how Centaur Labs is working with the Brigham and Women's Hospital team to develop multiple AI applications for point of care ultrasound.
Understand why traditional labeling pipelines are so hard to scale and learn how our solution can 10X your labeling pipeline in a shorter time frame and with higher accuracy
Access a collection of dozens of open source image datasets for medical AI across a variety of formats including X-ray, CT, Ultrasound, Whole Slide Imagery, MRI and more
Examine the unique challenges with medical data labeling, the relative lack of accuracy produced by traditional data labeling methods, and discover a more accurate and scalable alternative
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