A comparative study of deep learning for cortical lesion MRI segmentation with explainability analysis in multiple sclerosis.

Molchanova N, Cagol A, Ocampo-Pineda M, Lu PJ, Weigel M, Chen X, Beck ES, Tsagkas C, Reich DS, Bulcke CV, Stölting A, Borrelli S, Maggi P, Lugo SB, Lemay DR, Depeursinge A, Granziera C, Müller H, Gordaliza PM, Bach Cuadra M

Open source

DOI
10.1016/j.nicl.2026.104007
Published
2026
Container
NeuroImage. Clinical
Publisher
Not recorded
Open access
yes

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BibTeX

@article{allodium:10.1016/j.nicl.2026.104007,
  title = {A comparative study of deep learning for cortical lesion MRI segmentation with explainability analysis in multiple sclerosis.},
  author = {Molchanova N and Cagol A and Ocampo-Pineda M and Lu PJ and Weigel M and Chen X and Beck ES and Tsagkas C and Reich DS and Bulcke CV and Stölting A and Borrelli S and Maggi P and Lugo SB and Lemay DR and Depeursinge A and Granziera C and Müller H and Gordaliza PM and Bach Cuadra M},
  year = {2026},
  journal = {NeuroImage. Clinical},
  doi = {10.1016/j.nicl.2026.104007},
  url = {https://doi.org/10.1016/j.nicl.2026.104007}
}

RIS

TY  - JOUR
TI  - A comparative study of deep learning for cortical lesion MRI segmentation with explainability analysis in multiple sclerosis.
AU  - Molchanova N
AU  - Cagol A
AU  - Ocampo-Pineda M
AU  - Lu PJ
AU  - Weigel M
AU  - Chen X
AU  - Beck ES
AU  - Tsagkas C
AU  - Reich DS
AU  - Bulcke CV
AU  - Stölting A
AU  - Borrelli S
AU  - Maggi P
AU  - Lugo SB
AU  - Lemay DR
AU  - Depeursinge A
AU  - Granziera C
AU  - Müller H
AU  - Gordaliza PM
AU  - Bach Cuadra M
PY  - 2026
JO  - NeuroImage. Clinical
DO  - 10.1016/j.nicl.2026.104007
UR  - https://doi.org/10.1016/j.nicl.2026.104007
ER  - 

APA

N, M., A, C., M, O., PJ, L., M, W., X, C., ES, B., C, T., DS, R., CV, B., A, S., S, B., P, M., SB, L., DR, L., A, D., C, G., H, M., PM, G., & M, B. C. (2026). A comparative study of deep learning for cortical lesion MRI segmentation with explainability analysis in multiple sclerosis.. NeuroImage. Clinical. https://doi.org/10.1016/j.nicl.2026.104007

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