Kartezio: Evolutionary Design of Explainable Pipelines for Biomedical Image Analysis

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...

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Bibliographic Details
Published in:arXiv.org 2023-09
Main Authors: Cortacero, Kévin, McKenzie, Brienne, Müller, Sabina, Khazen, Roxana, Lafouresse, Fanny, Corsaut, Gaëlle, Nathalie Van Acker, François-Xavier Frenois, Lamant, Laurence, Meyer, Nicolas, Vergier, Béatrice, Wilson, Dennis G, Luga, Hervé, Staufer, Oskar, Dustin, Michael L, Valitutti, Salvatore, Cussat-Blanc, Sylvain
Format: Article
Language:English
Subjects:
Cartesian coordinates
Computer vision
Datasets
Deep learning
Flexibility
Genetic algorithms
Image analysis
Image annotation
Image processing
Image segmentation
Machine learning
Medical imaging
Semantics
Training
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Summary: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. Crucially however, these frameworks require large human-annotated datasets for training and the resulting models are difficult to interpret. In this study, we introduce Kartezio, a modular Cartesian Genetic Programming based computational strategy that generates 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, a feature which confers tremendous flexibility, speed, and functionality to this approach. We also deployed Kartezio to solve semantic and instance segmentation problems in four real-world Use Cases, and showcase its utility in imaging contexts ranging from high-resolution microscopy to clinical pathology. By successfully implementing Kartezio on a portfolio of images ranging from subcellular structures to tumoral tissue, we demonstrated the flexibility, robustness and practical utility of this fully explicable evolutionary designer for semantic and instance segmentation.
ISSN:2331-8422
DOI:10.48550/arxiv.2302.14762