Evolutionary design of explainable algorithms for biomedical image segmentation
Created by W.Langdon from
gp-bibliography.bib Revision:1.7428
- @Article{Cortacero:2023:NatCommun,
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author = "Kevin Cortacero and Brienne McKenzie and
Sabina Mueller and Roxana Khazen and Fanny Lafouresse and
Gaelle Corsaut and Nathalie {Van Acker} and
Francois-Xavier Frenois and Laurence Lamant and
Nicolas Meyer and Beatrice Vergier and Dennis G. Wilson and
Herve Luga and Oskar Staufer and Michael L. Dustin and
Salvatore Valitutti and Sylvain Cussat-Blanc",
-
title = "Evolutionary design of explainable algorithms for
biomedical image segmentation",
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journal = "Nature Communications",
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year = "2023",
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volume = "14",
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pages = "article 7112",
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month = "06 " # nov,
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keywords = "genetic algorithms, genetic programming, Cartesian
Genetic Programming, Kartezio, Cytotoxic T cells, Image
processing, Machine learning",
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ISSN = "2041-1723",
-
URL = "
https://rdcu.be/dqvF0",
-
DOI = "
doi:10.1038/s41467-023-42664-x",
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code_url = "
https://github.com/KevinCortacero/Kartezio",
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size = "18 pages",
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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 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
parameterising 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.",
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notes = "filed patent; EP 22307041.8
Leibniz Institute for New Materials, 66123,
Saarbruecken, Germany",
- }
Genetic Programming entries for
Kevin Cortacero
Brienne McKenzie
Sabina Mueller
Roxana Khazen
Fanny Lafouresse
Gaelle Corsaut
Nathalie Van Acker
Francois-Xavier Frenois
Laurence Lamant
Nicolas Meyer
Beatrice Vergier
Dennis G Wilson
Herve Luga
Oskar Staufer
Michael L Dustin
Salvatore Valitutti
Sylvain Cussat-Blanc
Citations