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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
