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AI-Assisted Quality Control of CTD Data
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Use Case Objectives
+ Machine Learning Task: Flag in advance the scans to be deleted during CTD quality control + Business Value: Flagged scans allow the analyst to quickly focus attention on crucial areas, reducing the time and effort required to delete scans + Measures of Success:
+ Accuracy of model predictions + Client feedback on quality control speed-ups
- Aspirational Goals:
* Mitigation of uncertainty in human decisions * Semi or full automation of scan deletions
Machine Learning Pipeline
Experimental Model Performance
Model Deployment and Integration
