In our case, the domain expert was a dentist who reached out to us to help him create a machine learning model that would segment teeth in panoramic X-rays. He had some data pre-labeled, but the vast majority of his dataset was unlabeled.
Since labeling these X-rays is a time consuming process and requires domain knowledge, we decided to use Active Learning.
Following our success in creating an Active Learning pipeline in a Jupyter Notebook using Data Engine, we created a new Tooth Fairy project, which expands on that and brings even more capabilities into the notebook.
https://dagshub.com/blog/active-learning-with-domain-experts-a-case-study/
Check out our post and learn: * Why and when you should use Active Learning * How to efficiently work with domain experts (and mistakes to avoid!) * What a real use-case Active Learning pipeline looks like, by checking out the accompanying repo
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