Interpretability of Uncertainty: Exploring Cortical Lesion Segmentation in Multiple Sclerosis
It addresses interpretability challenges in medical AI for clinicians, but is incremental as it applies existing uncertainty quantification methods to a specific domain.
This study tackled the problem of interpreting uncertainty in deep learning models for cortical lesion segmentation in multiple sclerosis MRI, showing that instance-wise uncertainty values can provide post hoc global model explanations and serve as a sanity check.
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.