Evidence map›Paper›PMID 40735108›Full record

ArticleArXiv2025

Explaining Uncertainty in Multiple Sclerosis Lesion Segmentation Beyond Prediction Errors.

Nataliia Molchanova, Pedro M Gordaliza, Alessandro Cagol, Mario Ocampo-Pineda, Po-Jui Lu, Matthias Weigel, Xinjie Chen, Erin S Beck, Haris Tsagkas, Daniel Reich and 7 more

Abstract readPreprint
PubMed Full text at PMC
In one paragraph

Article in ArXiv, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Nataliia MolchanovaFaculty of Biology and Medicine, University of Lausanne (UNIL), Lausanne, Switzerland.
Pedro M GordalizaCIBM Center for Biomedical Imaging, Lausanne, Switzerland.
Alessandro CagolTranslational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.
Mario Ocampo-PinedaTranslational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.
Po-Jui LuTranslational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.
Matthias WeigelTranslational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.
Xinjie ChenTranslational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.
Erin S BeckDepartment of Neurology, Icahn School of Medicine at Mount Sinai, New York City, USA.
Haris TsagkasTranslational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, USA.
Daniel ReichTranslational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, USA.
Anna StöltingNeuroinflammation Imaging Lab (NIL), Université catholique de Louvain, Brussels, Belgium.
Pietro MaggiNeuroinflammation Imaging Lab (NIL), Université catholique de Louvain, Brussels, Belgium.
Delphine RibesEPFL+ECAL Lab, École polytechnique fédérale de Lausanne (EPFL), Lausanne, Switzerland.
Adrien DepeursingeMedGIFT, Institute of Informatics, School of Management, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland.
Cristina GranzieraTranslational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.
Henning MüllerMedGIFT, Institute of Informatics, School of Management, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland.
Meritxell Bach CuadraCIBM Center for Biomedical Imaging, Lausanne, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Trustworthy artificial intelligence (AI) is essential in healthcare, particularly for high-stakes tasks like medical image segmentation. Explainable AI and uncertainty quantification significantly enhance AI reliability by addressing key attributes such as robustness, usability, and explainability. Despite extensive technical advances in uncertainty quantification for medical imaging, understanding the clinical informativeness and interpretability of uncertainty remains limited. This study introduces a novel framework to explain the potential sources of predictive uncertainty, specifically in cortical lesion segmentation in multiple sclerosis using deep ensembles. The proposed analysis shifts the focus from the uncertainty-error relationship towards relevant medical and engineering factors. Our findings reveal that instance-wise uncertainty is strongly related to lesion size, shape, and cortical involvement. Expert rater feedback confirms that similar factors impede annotator confidence. Evaluations conducted on two datasets (206 patients, almost 2000 lesions) under both in-domain and distribution-shift conditions highlight the utility of the framework in different scenarios.

Indexed as

Explainable AIExplained uncertaintyInstance–wise uncertaintyLesion segmentationMagnetic resonance imagingMultiple sclerosisUncertainty quantification

Identifiers

PMID40735108
PMCPMC12306819

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-SA
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.