Tree-Based Codification in Neural Architecture Search for Medical Image Segmentation

Created by W.Langdon from gp-bibliography.bib Revision:1.8010

@Article{Fuentes-Tomas:TEVC,

• author = "Jose-Antonio Fuentes-Tomas and Efren Mezura-Montes and Hector-Gabriel Acosta-Mesa and Aldo Marquez-Grajales",
• journal = "IEEE Transactions on Evolutionary Computation",
• title = "Tree-Based Codification in Neural Architecture Search for Medical Image Segmentation",
• note = "Early access",
• abstract = "Convolutional Neural Networks (CNNs) have shown a competitive performance in medical imaging applications, such as image segmentation. However, choosing an existing architecture capable of adapting to a specific dataset is challenging and requires design expertise. Neural Architecture Search (NAS) is employed to overcome these limitations. NAS uses techniques to design the Neural Networks architecture. Typically, the models' weights optimisation is carried out using a continuous loss function, unlike model topology optimisation, which is highly influenced by the specific problem. Genetic Programming (GP) is an Evolutionary Algorithm (EA) capable of adapting to the topology optimisation problem of CNNs by considering the attributes of its representation. A tree representation can express complex connectivity and apply variation operations. This paper presents a tree-based GP algorithm for evolving CNNs based on the well-known U-Net architecture producing compact and flexible models for medical image segmentation across multiple domains. This proposal is called Neural Architecture Search / Genetic Programming / U-Net (NASGP-Net). NASGP-Net uses a cell-based encoding and U-Net architecture as a backbone to construct CNNs based on a hierarchical arrangement of primitive operations. Our experiments indicate that our approach can produce remarkable segmentation results with fewer parameters regarding fixed architectures. Moreover, NASGP-Net presents competitive results against NAS methods. Finally, we observed notable performance improvements based on several evaluation metrics, including Dice similarity coefficient (DSC), Intersection over union (IoU), and Hausdorff Distance (HD).",
• keywords = "genetic algorithms, genetic programming, Image segmentation, Computer architecture, Biomedical imaging, Statistics, Sociology, Convolution, Syntactics, Neural Architecture Search, ANN, Convolutional Neural Networks, Medical Image Segmentation",
• DOI = "doi:10.1109/TEVC.2024.3353182",
• ISSN = "1941-0026",
• notes = "Also known as \cite{10391062}",

}

Genetic Programming entries for Jose-Antonio Fuentes-Tomas Efren Mezura-Montes Hector-Gabriel Acosta-Mesa Aldo Marquez-Grajales

Citations