PI Yuan Tian discusses the Dprov project: A Data Provenance Framework for Medical Machine Learning. In this talk Tian reviews the needs for data integrity, provence and authenticity. Integrity involves detecting public ML models trained on corrupt data, Provence involves establishing a Standardize efficient, reproducible dataset-model tracking. Authenticity involves removing patient or corrupt data from models effectively. Tian further discusses data set requirements.

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