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Evolutionary design of explainable algorithms for biomedical image segmentation
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 experts in i...
Autores principales: | , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10628266/ https://www.ncbi.nlm.nih.gov/pubmed/37932311 http://dx.doi.org/10.1038/s41467-023-42664-x |
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author | Cortacero, Kévin McKenzie, Brienne Müller, Sabina Khazen, Roxana Lafouresse, Fanny Corsaut, Gaëlle Van Acker, Nathalie Frenois, François-Xavier Lamant, Laurence Meyer, Nicolas Vergier, Béatrice Wilson, Dennis G. Luga, Hervé Staufer, Oskar Dustin, Michael L. Valitutti, Salvatore Cussat-Blanc, Sylvain |
author_facet | Cortacero, Kévin McKenzie, Brienne Müller, Sabina Khazen, Roxana Lafouresse, Fanny Corsaut, Gaëlle Van Acker, Nathalie Frenois, François-Xavier Lamant, Laurence Meyer, Nicolas Vergier, Béatrice Wilson, Dennis G. Luga, Hervé Staufer, Oskar Dustin, Michael L. Valitutti, Salvatore Cussat-Blanc, Sylvain |
author_sort | Cortacero, Kévin |
collection | PubMed |
description | 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 experts in image segmentation tasks. However, these frameworks require large human-annotated datasets for training and the resulting “black box” models are difficult to interpret. In this study, we introduce Kartezio, a modular Cartesian Genetic Programming-based computational strategy that generates fully transparent and easily interpretable image processing pipelines by iteratively assembling and parameterizing computer vision functions. The pipelines thus generated exhibit comparable precision to state-of-the-art Deep Learning approaches on instance segmentation tasks, while requiring drastically smaller training datasets. This Few-Shot Learning method confers tremendous flexibility, speed, and functionality to this approach. We then deploy Kartezio to solve a series of semantic and instance segmentation problems, and demonstrate its utility across diverse images ranging from multiplexed tissue histopathology images to high resolution microscopy images. While the flexibility, robustness and practical utility of Kartezio make this fully explicable evolutionary designer a potential game-changer in the field of biomedical image processing, Kartezio remains complementary and potentially auxiliary to mainstream Deep Learning approaches. |
format | Online Article Text |
id | pubmed-10628266 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-106282662023-11-08 Evolutionary design of explainable algorithms for biomedical image segmentation Cortacero, Kévin McKenzie, Brienne Müller, Sabina Khazen, Roxana Lafouresse, Fanny Corsaut, Gaëlle Van Acker, Nathalie Frenois, François-Xavier Lamant, Laurence Meyer, Nicolas Vergier, Béatrice Wilson, Dennis G. Luga, Hervé Staufer, Oskar Dustin, Michael L. Valitutti, Salvatore Cussat-Blanc, Sylvain Nat Commun Article 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 experts in image segmentation tasks. However, these frameworks require large human-annotated datasets for training and the resulting “black box” models are difficult to interpret. In this study, we introduce Kartezio, a modular Cartesian Genetic Programming-based computational strategy that generates fully transparent and easily interpretable image processing pipelines by iteratively assembling and parameterizing computer vision functions. The pipelines thus generated exhibit comparable precision to state-of-the-art Deep Learning approaches on instance segmentation tasks, while requiring drastically smaller training datasets. This Few-Shot Learning method confers tremendous flexibility, speed, and functionality to this approach. We then deploy Kartezio to solve a series of semantic and instance segmentation problems, and demonstrate its utility across diverse images ranging from multiplexed tissue histopathology images to high resolution microscopy images. While the flexibility, robustness and practical utility of Kartezio make this fully explicable evolutionary designer a potential game-changer in the field of biomedical image processing, Kartezio remains complementary and potentially auxiliary to mainstream Deep Learning approaches. Nature Publishing Group UK 2023-11-06 /pmc/articles/PMC10628266/ /pubmed/37932311 http://dx.doi.org/10.1038/s41467-023-42664-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Cortacero, Kévin McKenzie, Brienne Müller, Sabina Khazen, Roxana Lafouresse, Fanny Corsaut, Gaëlle Van Acker, Nathalie Frenois, François-Xavier Lamant, Laurence Meyer, Nicolas Vergier, Béatrice Wilson, Dennis G. Luga, Hervé Staufer, Oskar Dustin, Michael L. Valitutti, Salvatore Cussat-Blanc, Sylvain Evolutionary design of explainable algorithms for biomedical image segmentation |
title | Evolutionary design of explainable algorithms for biomedical image segmentation |
title_full | Evolutionary design of explainable algorithms for biomedical image segmentation |
title_fullStr | Evolutionary design of explainable algorithms for biomedical image segmentation |
title_full_unstemmed | Evolutionary design of explainable algorithms for biomedical image segmentation |
title_short | Evolutionary design of explainable algorithms for biomedical image segmentation |
title_sort | evolutionary design of explainable algorithms for biomedical image segmentation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10628266/ https://www.ncbi.nlm.nih.gov/pubmed/37932311 http://dx.doi.org/10.1038/s41467-023-42664-x |
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