Author

Nataliia Molchanova

HES-SO University of Applied Sciences and Arts Western SwitzerlandORCID 0000-0002-7211-8863Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioengineering· 1 Neuroscience· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Approach:statistical model· 2 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Research Focus:differential expression· 1 Assay:imaging· 1 Assay:multiome· 1

Frequent Co-Authors

Cristina GranzieraiD· 2 shared Meritxell Bach CuadraiD· 2 shared Adrien DepeursingeiD· 1 shared Alessandro CagoliD· 1 shared Federico SpagnoloiD· 1 shared Jaume BanúsiD· 1 shared Joost JansseniD· 1 shared Lester Melie‐GarcíaiD· 1 shared

Activity by Year

2024-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

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2 shared papersNeuroscience

Cristina GranzieraORCID

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2 papers

Recent·Most cited

NeuroscienceLecture notes in computer sciencePeer-reviewedJan 1, 2025

Towards Longitudinal Characterization of Multiple Sclerosis Atrophy Employing SynthSeg Framework and Normative Modeling

Pedro M. Gordaliza, Nataliia Molchanova, Maxence Wynen…·Meritxell Bach Cuadra

This study uses deep learning and normative modeling to analyze MRI data from MS patients and identify subcortical volume deviations that improve understanding of disease progression.

1 citationtop 2%

multiomedeep learning

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BioengineeringbioRxivPreprintAug 29, 2024

Exploiting XAI maps to improve MS lesion segmentation and detection in MRI

Federico Spagnolo, Nataliia Molchanova, Mario Ocampo‐Pineda…·Adrien Depeursinge

This paper improves MS lesion segmentation and detection in MRI by using saliency maps to refine prediction scores, achieving higher F1 and PPV scores.

DOI 10.1101/2024.08.29.610090

imagingdeep learning

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