Kartezio: Evolutionary Design of Explainable Pipelines for Biomedical Image Analysis

• URL: http://arxiv.org/abs/2302.14762v2
• Date: Fri, 22 Sep 2023 18:14:40 GMT
• Title: Kartezio: Evolutionary Design of Explainable Pipelines for Biomedical Image Analysis
• Authors: K\'evin Cortacero, Brienne McKenzie, Sabina M\"uller, Roxana Khazen, Fanny Lafouresse, Ga\"elle Corsaut, Nathalie Van Acker, Fran\c{c}ois-Xavier Frenois, Laurence Lamant, Nicolas Meyer, B\'eatrice Vergier, Dennis G. Wilson, Herv\'e Luga, Oskar Staufer, Michael L. Dustin, Salvatore Valitutti and Sylvain Cussat-Blanc
• Abstract summary: We introduce Kartezio, a computational strategy that generates transparent and easily interpretable image processing pipelines. The pipelines thus generated exhibit comparable precision to state-of-the-art Deep Learning approaches on instance segmentation tasks. We also deployed Kartezio to solve semantic and instance segmentation problems in four real-world Use Cases.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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. 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.

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