Related papers: Interpretability of Uncertainty: Exploring Cortical Lesion Segmentation in Multiple Sclerosis

Interpretability of Uncertainty: Exploring Cortical Lesion Segmentation in Multiple Sclerosis

URL: http://arxiv.org/abs/2407.05761v1
Date: Mon, 8 Jul 2024 09:13:30 GMT
Title: Interpretability of Uncertainty: Exploring Cortical Lesion Segmentation in Multiple Sclerosis
Authors: Nataliia Molchanova, Alessandro Cagol, Pedro M. Gordaliza, Mario Ocampo-Pineda, Po-Jui Lu, Matthias Weigel, Xinjie Chen, Adrien Depeursinge, Cristina Granziera, Henning Müller, Meritxell Bach Cuadra,
Abstract summary: Uncertainty quantification (UQ) has become critical for evaluating the reliability of artificial intelligence systems. This study addresses the interpretability of instance-wise uncertainty values in deep learning models for focal lesion segmentation in magnetic resonance imaging.
Score: 33.91263917157504
License: http://creativecommons.org/licenses/by-sa/4.0/
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.