Understand how Centaur Labs' data annotation platform offers richer results than traditional data labeling vendors to improve your medical AI pipeline
With the demand for medical AI increasing rapidly, so has the need for accurate and scalable medical data labeling solutions. In recent years, three models for acquiring medical data labels have emerged:
As cited in our white paper, these traditional models fail to deliver highly accurate results. What’s more, the reasoning behind the labeling results is somewhat of a ‘black box’ because no information about labels like confidence and case difficulty is provided, which makes QCing data labels extremely difficult.
In contrast, labels produced through Centaur Labs’ collective intelligence approach can not only provide high accuracy, but also provide deep insights into the labels produced. The additional signal produced by collecting and aggregating multiple opinions from experts opens the door to several beneficial use cases.
If you have a dataset with labels of uncertain quality because they were produced by a single expert, an outsourced labeling vendor, or pre-labeled by an AI model, we can analyze each label and our network of experts will flag any that are incorrect. This use case was inspired by the joke among radiologists where “every time a radiologist looks at a scan they find a new nodule"
Since we collect multiple expert opinions for each case, we are able to develop a much richer understanding of the quality of the data. One particular insight we can measure is the level of agreement/disagreement between our experts for a specific case.
Difficulty scores are reported on our platform for each case labeled:
Clients use the difficulty score in a few different ways, depending on their specific situation and goals:
Lastly, we provide customers with granular information about each labeler read. We share the number of votes for each answer choice in the case of classification or coordinates for each annotation in the case of segmentation.
We also gather individual labeler accuracy scores. We know the correct answer to some portion of the cases viewed by each labeler, so we’re able to calculate individual accuracy at any given task.
Getting multiple opinions doesn’t just give you more accurate answers, it also gives you more insight into your data that can be used to optimize your data pipeline and improve the accuracy of your models. Curious to give our platform a try? Create an account to preview our portal to explore sample datasets and download example results of medical data created using collective intelligence.
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The model recommends patients for partial (UKA) or total (UKA) knee arthroplasty with high confidence, based on standard knee x-ray views.