Interpretability of Uncertainty: Exploring Cortical Lesion Segmentation in Multiple Sclerosis

Nataliia Molchanova, Alessandro Cagol, Pedro M. Gordaliza, Mario Ocampo-Pineda, Po-Jui Lu +6 more
7/8/2024
eess.IVcs.CV

Official code: NataliiaMolch/interpret-lesion-unc (3 stars, last commit 2026-04-21).

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.

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Code Implementations(1)

NataliiaMolch/interpret-lesion-uncOfficial100%

Multiple sclerosis cortical lesion segmentation model on MP2RAGE - DE and MCDP uncertainty quantification - Lesion-scale uncertainty - Instance-wise uncertainty - Interpretability of lesion-scale uncertainty - Interpretability of instance-wise uncertainty

33PythonJul 8, 20246 months agoNOASSERTION

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Cite this paper

@article{molchanova2024interpretability,
  title  = {Interpretability of Uncertainty: Exploring Cortical Lesion Segmentation in Multiple Sclerosis},
  author = {Nataliia Molchanova and Alessandro Cagol and Pedro M. Gordaliza and Mario Ocampo-Pineda and Po-Jui Lu and Matthias Weigel and Xinjie Chen and Adrien Depeursinge and Cristina Granziera and Henning Müller and Meritxell Bach Cuadra},
  year   = {2024},
  eprint = {2407.05761},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2407.05761v1}
}

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