Interpretability of Uncertainty: Exploring Cortical Lesion Segmentation in Multiple Sclerosis

TL;DR

This paper investigates how uncertainty quantification in deep learning models for cortical lesion segmentation in multiple sclerosis can enhance interpretability, model reliability, and bias detection in medical imaging.

Contribution

It demonstrates the use of instance-wise uncertainty for post hoc explanations and reliability assessment in cortical lesion segmentation models.

Findings

Uncertainty helps identify model biases.

UQ provides insights into model behavior.

Method improves trustworthiness of segmentation.

Abstract

Uncertainty quantification (UQ) has become critical for evaluating the reliability of artificial intelligence systems, especially in medical image segmentation. This study addresses the interpretability of instance-wise uncertainty values in deep learning models for focal lesion segmentation in magnetic resonance imaging, specifically cortical lesion (CL) segmentation in multiple sclerosis. CL segmentation presents several challenges, including the complexity of manual segmentation, high variability in annotation, data scarcity, and class imbalance, all of which contribute to aleatoric and epistemic uncertainty. We explore how UQ can be used not only to assess prediction reliability but also to provide insights into model behavior, detect biases, and verify the accuracy of UQ methods. Our research demonstrates the potential of instance-wise uncertainty values to offer post hoc global model explanations, serving as a sanity check for the model. The implementation is available at https://github.com/NataliiaMolch/interpret-lesion-unc.