Abstract
The design of neural architecture to address the challenge of detecting abnormalities in histopathology images can leverage the gains made in the field of neural architecture search (NAS). The NAS model consists of a search space, search strategy and evaluation strategy. The approach supports the automation of deep learning (DL) based networks such as convolutional neural networks (CNN). Automating the process of CNN architecture engineering using this approach allows for finding the best performing network for learning classification problems in specific domains and datasets. However, the engineering process of NAS is often limited by the potential solutions in search space and the search strategy. This problem often narrows the possibility of obtaining best performing networks for challenging tasks such as the classification of breast cancer in digital histopathological samples. This study proposes a NAS model with a novel search space initialization algorithm and a new search strategy. We designed a block-based stochastic categorical-to-binary (BSCB) algorithm for generating potential CNN solutions into the search space. Also, we applied and investigated the performance of a new bioinspired optimization algorithm, namely the Ebola optimization search algorithm (EOSA), for the search strategy. The evaluation strategy was achieved through computation of loss function, architectural latency and accuracy. The results obtained using images from the BACH and BreakHis databases showed that our approach obtained best performing architectures with the top-5 of the architectures yielding a significant detection rate. The top-1 CNN architecture demonstrated a state-of-the-art performance of base on classification accuracy. The NAS strategy applied in this study and the resulting candidate architecture provides researchers with the most appropriate or suitable network configuration for using digital histopathology.
Highlights
IntroductionAutomating the process of convolutional neural networks (CNN) architecture engineering using this approach allows for finding the best performing network for learning classification problems in specific domains and datasets
The design of neural architecture to address the challenge of detecting abnormalities in histopathology images can leverage the gains made in the field of neural architecture search (NAS)
These same functions were applied to artificial bee colony (ABC), whale optimization algorithm (WOA), particle swarm optimization (PSO), and genetic algorithm (GA) metaheuristic algorithms
Summary
Automating the process of CNN architecture engineering using this approach allows for finding the best performing network for learning classification problems in specific domains and datasets. The engineering process of NAS is often limited by the potential solutions in search space and the search strategy This problem often narrows the possibility of obtaining best performing networks for challenging tasks such as the classification of breast cancer in digital histopathological samples. The NAS technique allows for the design of high-performing models by using search strategy based on RL or optimization algorithms to search and design neural architectures. Initial candidate solutions (neural architectures) are generated based on a constrained formal definition of a search space allowing the search strategy to apply an evaluation function in realigning the networks during iteration. The application of the NAS approach in designing the best performing neural architecture for this task remains promising
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