Benchmarking Scalable Epistemic Uncertainty Quantification in Organ Segmentation.

Area of Science:

• Medical image analysis
• Artificial intelligence in healthcare
• Computational pathology

Background:

• Deep learning excels at automatic organ segmentation for diagnosis and treatment planning.
• Quantifying prediction uncertainty is vital for clinical applications of AI.
• Current epistemic uncertainty methods lack clear preference in medical imaging.

Purpose of the Study:

• To benchmark epistemic uncertainty quantification methods for organ segmentation.
• To evaluate methods based on accuracy, calibration, and scalability.
• To provide guidance for developing reliable clinical AI models.

Main Methods:

• Comprehensive benchmarking of various epistemic uncertainty estimation techniques.
• Evaluation of segmentation accuracy and uncertainty calibration.
• Assessment of scalability and out-of-distribution detection capabilities.

Main Results:

• Comparative analysis of strengths and weaknesses of different uncertainty methods.
• Identification of preferred methods for specific clinical scenarios.
• Insights into improving robustness and reliability of AI in medical imaging.

Conclusions:

• Benchmarking provides critical insights into epistemic uncertainty quantification for organ segmentation.
• Findings aid in selecting appropriate methods for clinical deployment.
• Recommendations for future research to enhance AI model reliability.