EMONAS-Net: Efficient multiobjective neural architecture search using surrogate-assisted evolutionary algorithm for 3D medical image segmentation

Baldeon-Calisto, María; Lai-Yuen, Susana K.

doi:10.1016/j.artmed.2021.102154

Cited by 31 publications

(9 citation statements)

References 21 publications

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“…Image classification [2,10,47,48,55,[145][146][147] and image segmentation [8,24,25,70,71,[148][149][150] are the most widely used fields of NAS in healthcare. In the traditional working mode, doctors often use the naked eye to scan under the microscope for judgment, use the manual experience to find the lesions, and then use the high-power lens for review, diagnosis, classification, and decision, which is a complicated process.…”

Section: Nas For Healthcare Applicationmentioning

confidence: 99%

“…For skin lesions, Anupama et al [148] proposed backtracking search optimization algorithm with entropy-based thresholding (BSA-EBT) to improve image segmentation accuracy. For prostate cancer and cardiology, Calisto et al [149] proposed a surrogate-assisted multiobjective evolutionary algorithm (SaMEA) for minimizing the architecture size and search time, aiming to complete 3D image segmentation. Baldeon et al [71] used a multiobjective adaptive CNN (AdaResU-Net) to optimize the performance and size of the model.…”

Section: Nas For Classification Of Disease Diagnosismentioning

confidence: 99%

“…Secondly, evolutionary NAS can adapt to meet the multiple requirements of a given task. Multiobjective evolutionary NAS applications in healthcare can attain high accuracy, improve model performance and reduce model size [2,71,149,[153][154][155]. Large-scale multiobjective neuroevolution strategies [73,[110][111][112][113] and distributed parallel EAs strategies [80,91,121,137,138] can accelerate the evolution process and search process of NAS.…”

Section: Nas For Classification Of Disease Diagnosismentioning

confidence: 99%

See 2 more Smart Citations

Evolutionary Neural Architecture Search and Its Applications in Healthcare

Liu,

Li,

Zhao

et al. 2024

CMES

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Most of the neural network architectures are based on human experience, which requires a long and tedious trial-and-error process. Neural architecture search (NAS) attempts to detect effective architectures without human intervention. Evolutionary algorithms (EAs) for NAS can find better solutions than human-designed architectures by exploring a large search space for possible architectures. Using multiobjective EAs for NAS, optimal neural architectures that meet various performance criteria can be explored and discovered efficiently. Furthermore, hardware-accelerated NAS methods can improve the efficiency of the NAS. While existing reviews have mainly focused on different strategies to complete NAS, a few studies have explored the use of EAs for NAS. In this paper, we summarize and explore the use of EAs for NAS, as well as large-scale multiobjective optimization strategies and hardware-accelerated NAS methods. NAS performs well in healthcare applications, such as medical image analysis, classification of disease diagnosis, and health monitoring. EAs for NAS can automate the search process and optimize multiple objectives simultaneously in a given healthcare task. Deep neural network has been successfully used in healthcare, but it lacks interpretability. Medical data is highly sensitive, and privacy leaks are frequently reported in the healthcare industry. To solve these problems, in healthcare, we propose an interpretable neuroevolution framework based on federated learning to address search efficiency and privacy protection. Moreover, we also point out future research directions for evolutionary NAS. Overall, for researchers who want to use EAs to optimize NNs in healthcare, we analyze the advantages and disadvantages of doing so to provide detailed guidance, and propose an interpretable privacy-preserving framework for healthcare applications.

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Section: Nas For Healthcare Applicationmentioning

confidence: 99%

Section: Nas For Classification Of Disease Diagnosismentioning

confidence: 99%

Section: Nas For Classification Of Disease Diagnosismentioning

confidence: 99%

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Evolutionary Neural Architecture Search and Its Applications in Healthcare

Liu,

Li,

Zhao

et al. 2024

CMES

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“…Calisto & Lai-Yuen [ 38 ] used the encoder/decoder framework to search for the best architecture for the segmentation of MRI. This network utilized two components 1) Search Procedure, and 2) Macro search structure [ 86 ].…”

Section: Introductionmentioning

confidence: 99%

Reviewing 3D convolutional neural network approaches for medical image segmentation

Ilesanmi,

Ajayi

2024

Heliyon

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“…However, due to the high inter-and intraobserver variability among practitioners, interpreting the image can be difficult and frequently inconsistent. By recognizing and classifying patterns in medical images, Deep Neural Networks, and in particular Convolutional Neural Networks (CNNs), have transformed automated disease detection [4][5][6][7]. As a result, several researchers have proposed CNNs designed specifically for COVID-19 chest X-ray classification.…”

Section: -Introductionmentioning

confidence: 99%

Analyzing the Effect of Basic Data Augmentation for COVID-19 Detection through a Fractional Factorial Experimental Design

et al. 2022

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The COVID-19 pandemic has created a worldwide healthcare crisis. Convolutional Neural Networks (CNNs) have recently been used with encouraging results to help detect COVID-19 from chest X-ray images. However, to generalize well to unseen data, CNNs require large labeled datasets. Due to the lack of publicly available COVID-19 datasets, most CNNs apply various data augmentation techniques during training. However, there has not been a thorough statistical analysis of how data augmentation operations affect classification performance for COVID-19 detection. In this study, a fractional factorial experimental design is used to examine the impact of basic augmentation methods on COVID-19 detection. The latter enables identifying which particular data augmentation techniques and interactions have a statistically significant impact on the classification performance, whether positively or negatively. Using the CoroNet architecture and two publicly available COVID-19 datasets, the most common basic augmentation methods in the literature are evaluated. The results of the experiments demonstrate that the methods of zoom, range, and height shift positively impact the model's accuracy in dataset 1. The performance of dataset 2 is unaffected by any of the data augmentation operations. Additionally, a new state-of-the-art performance is achieved on both datasets by training CoroNet with the ideal data augmentation values found using the experimental design. Specifically, in dataset 1, 97% accuracy, 93% precision, and 97.7% recall were attained, while in dataset 2, 97% accuracy, 97% precision, and 97.6% recall were achieved. These results indicate that analyzing the effects of data augmentations on a particular task and dataset is essential for the best performance. Doi: 10.28991/ESJ-2023-SPER-01 Full Text: PDF

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EMONAS-Net: Efficient multiobjective neural architecture search using surrogate-assisted evolutionary algorithm for 3D medical image segmentation

Cited by 31 publications

References 21 publications

Evolutionary Neural Architecture Search and Its Applications in Healthcare

Evolutionary Neural Architecture Search and Its Applications in Healthcare

Reviewing 3D convolutional neural network approaches for medical image segmentation

Analyzing the Effect of Basic Data Augmentation for COVID-19 Detection through a Fractional Factorial Experimental Design

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