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.

翻译：COVID-19大流行已经在全球蔓延了数月。 由于其长期孕育期长和测试成本高,没有线索显示其传播速度正在放缓,因此迫切需要一种更快的测试方法。 本文提出一个高效的进化多目标神经神经神经神经搜索框架(EMARS),它可以自动搜索基于精心设计的COVID-19胸腔CT扫描分类搜索空间的3D神经结构。 在这个框架内,我们使用权重共享战略显著提高搜索效率,在8小时内完成搜索进程。 我们还提出了一个新目标,即潜力,这有利于提高搜索进程的稳健性。 有了准确性、潜力和模型大小的目标,我们找到了一个轻质模型(3.39 MB),它比三种人类设计的基线模型(即ResNet3D101 (325.21 MB)、DenseNet3D121 (43.06 MB)和MC3 ⁇ 18 (43.84 MB ), 并且我们精心设计的搜索空间使得班级启动绘图的算法能够轻松地将所有可视力分析模型嵌入所有可视模型。