Evolutionary design of explainable algorithms for biomedical image segmentation

Evolutionary design of explainable algorithms for biomedical image segmentation

Abstract An unresolved issue in contemporary biomedicine is the overwhelming number and diversity of complex images that require annotation, analysis and interpretation. Recent advances in Deep Learning have revolutionized the field of computer vision, creating algorithms that compete with human exp...

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Bibliographic Details
Main Authors: Kévin Cortacero, Brienne McKenzie, Sabina Müller, Roxana Khazen, Fanny Lafouresse, Gaëlle Corsaut, Nathalie Van Acker, François-Xavier Frenois, Laurence Lamant, Nicolas Meyer, Béatrice Vergier, Dennis G. Wilson, Hervé Luga, Oskar Staufer, Michael L. Dustin, Salvatore Valitutti, Sylvain Cussat-Blanc
Format: Article
Language:English
Published: Nature Portfolio 2023-11-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-023-42664-x

Internet

https://doi.org/10.1038/s41467-023-42664-x

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