Statistical Guarantees Of False Discovery Rate In Medical Instance Segmentation Tasks Based on Conformal Risk Control
Mengxia Dai, Wenqian Luo, Tianyang Li·April 06, 2025
Summary
A conformal risk control framework for medical instance segmentation is introduced, addressing confidence calibration in high-risk applications. It proposes a calibration-aware loss function to adjust segmentation thresholds based on a user-defined risk level α, ensuring the expected false negative or false discovery rate remains below α with high probability. The framework is compatible with mainstream models and datasets without requiring architectural modifications, demonstrating rigorous bounding of the false discovery rate metric over the test set.
Introduction
Background
Overview of medical instance segmentation challenges
Importance of risk control in high-stakes applications
Objective
Introduce a novel framework for conformal risk control in medical instance segmentation
Address confidence calibration to manage false negatives and false discoveries
Method
Calibration-Aware Loss Function
Description of the loss function design
How it adjusts segmentation thresholds based on user-defined risk level α
Risk Control Mechanism
Explanation of how the framework ensures the expected false negative or false discovery rate remains below α with high probability
Compatibility and Implementation
Framework's adaptability to mainstream models and datasets
No requirement for architectural modifications
Performance Evaluation
Demonstration of rigorous bounding of the false discovery rate metric over the test set
Validation through comparison with existing methods
Results
Quantitative Analysis
Metrics used for evaluation
Comparison of performance with baseline methods
Qualitative Analysis
Case studies showcasing the framework's effectiveness
Examples of improved risk control in medical applications
Conclusion
Summary of Contributions
Recap of the framework's unique features and benefits
Future Work
Potential areas for further research and development
Implications
Impact on medical decision-making and patient safety
Basic info
papers
computer vision and pattern recognition
artificial intelligence
Advanced features
Insights
How does the framework ensure the false discovery rate remains below the user-defined risk level?
How does the conformal risk control framework integrate with existing medical instance segmentation models?
What is the role of the calibration-aware loss function in the proposed framework?
Can the framework be applied to different datasets without architectural changes?