Application-driven validation of posteriors in inverse problems.

Area of Science:

• Computer Vision
• Machine Learning
• Medical Imaging

Background:

• Deep learning models struggle with inverse problems having multiple valid solutions.
• Existing validation methods for posterior-based models do not align with practical application needs.

Purpose of the Study:

• To present the first systematic framework for application-driven validation of posterior-based methods in inverse problems.
• To address the gap in adequate validation for models dealing with multiple plausible solutions.

Main Methods:

• Developed a novel validation framework inspired by object detection principles.
• Introduced mode-centric validation using application-relevant metrics.
• Applied the framework to synthetic data and medical imaging use cases (surgical pose estimation, functional tissue parameter quantification).

Main Results:

• The proposed framework provides a more effective validation approach compared to common methods.
• Demonstrated the framework's utility and advantages in diverse inverse problem scenarios.
• Enabled interpretable, application-focused performance assessment for posterior-based methods.

Conclusions:

• The new framework revolutionizes performance assessment in inverse problems by enabling robust validation of multi-solution models.
• Facilitates the translation of advanced posterior-based methods like Diffusion Models and Invertible Neural Networks into practical applications.
• Offers a significant advancement for the reliable evaluation of AI in complex image analysis tasks.