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Automated Model Design and Benchmarking of 3D Deep Learning Models for COVID-19 Detection with Chest CT Scans
The COVID-19 pandemic has spread globally for several months. Because it...
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Multi-Objective Neural Architecture Search Based on Diverse Structures and Adaptive Recommendation
The search space of neural architecture search (NAS) for convolutional n...
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Multi-Objective Reinforced Evolution in Mobile Neural Architecture Search
Fabricating neural models for a wide range of mobile devices demands for...
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Multi-objective Neural Architecture Search with Almost No Training
In the recent past, neural architecture search (NAS) has attracted incre...
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ScarletNAS: Bridging the Gap Between Scalability and Fairness in Neural Architecture Search
One-shot neural architecture search features fast training of a supernet...
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Local Search is a Remarkably Strong Baseline for Neural Architecture Search
Neural Architecture Search (NAS), i.e., the automation of neural network...
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A Novel Training Protocol for Performance Predictors of Evolutionary Neural Architecture Search Algorithms
Evolutionary Neural Architecture Search (ENAS) can automatically design ...
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Efficient Multi-objective Evolutionary 3D Neural Architecture Search for COVID-19 Detection with Chest CT Scans
COVID-19 pandemic has spread globally for months. Due to its long incubation period and high testing cost, there is no clue showing its spread speed is slowing down, and hence a faster testing method is in dire need. This paper proposes an efficient Evolutionary Multi-objective neural ARchitecture Search (EMARS) framework, which can automatically search for 3D neural architectures based on a well-designed search space for COVID-19 chest CT scan classification. Within the framework, we use weight sharing strategy to significantly improve the search efficiency and finish the search process in 8 hours. We also propose a new objective, namely potential, which is of benefit to improve the search process's robustness. With the objectives of accuracy, potential, and model size, we find a lightweight model (3.39 MB), which outperforms three baseline human-designed models, i.e., ResNet3D101 (325.21 MB), DenseNet3D121 (43.06 MB), and MC3_18 (43.84 MB). Besides, our well-designed search space enables the class activation mapping algorithm to be easily embedded into all searched models, which can provide the interpretability for medical diagnosis by visualizing the judgment based on the models to locate the lesion areas.
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