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Difference between revisions of "AI-Assisted Quality Control of CTD Data"
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== Use Case Objectives == | == Use Case Objectives == | ||
| − | * Machine Learning Task: Flag in advance the scans to be deleted during CTD quality control | + | * '''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 | + | * '''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: | + | * '''Measures of Success''': |
** Accuracy of model predictions | ** Accuracy of model predictions | ||
**Client feedback on quality control speed-ups | **Client feedback on quality control speed-ups | ||
| − | *Aspirational Goals: | + | *'''Aspirational Goals''': |
** Mitigation of uncertainty in human decisions | ** Mitigation of uncertainty in human decisions | ||
** Semi or full automation of scan deletions | ** Semi or full automation of scan deletions | ||
Revision as of 10:09, 22 December 2022
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
