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 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-o

SUBMITTER: Cortacero K 

PROVIDER: S-EPMC10628266 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature