Application-driven Validation of Posteriors in Inverse Problems

TL;DR

This paper introduces a novel, application-driven framework for validating posterior-based methods in inverse problems, addressing the challenge of multiple plausible solutions in image analysis tasks, with demonstrated benefits in medical imaging applications.

Contribution

It presents the first systematic, object detection-inspired validation framework for posterior methods, enabling mode-centric evaluation aligned with practical application needs.

Findings

Framework improves validation accuracy in medical imaging tasks

Mode-centric validation offers better interpretability

Demonstrated effectiveness on synthetic and real-world examples

Abstract

Current deep learning-based solutions for image analysis tasks are commonly incapable of handling problems to which multiple different plausible solutions exist. In response, posterior-based methods such as conditional Diffusion Models and Invertible Neural Networks have emerged; however, their translation is hampered by a lack of research on adequate validation. In other words, the way progress is measured often does not reflect the needs of the driving practical application. Closing this gap in the literature, we present the first systematic framework for the application-driven validation of posterior-based methods in inverse problems. As a methodological novelty, it adopts key principles from the field of object detection validation, which has a long history of addressing the question of how to locate and match multiple object instances in an image. Treating modes as instances enables us to perform mode-centric validation, using well-interpretable metrics from the application perspective. We demonstrate the value of our framework through instantiations for a synthetic toy example and two medical vision use cases: pose estimation in surgery and imaging-based quantification of functional tissue parameters for diagnostics. Our framework offers key advantages over common approaches to posterior validation in all three examples and could thus revolutionize performance assessment in inverse problems.