Lung cancer has caused the most cancer deaths in the past several years. Benign–malignant lung nodule classification is vital in lung nodule detection, which can help early diagnosis of lung cancer. Most existing works extract the features of chest CT images using the well-designed networks, which require substantial effort of experts. To automate the manual process of network design, we propose an attention-guided differentiable architecture search (A-DARTS) method, which directly searches for the optimal network on chest CT images. In addition, A-DARTS utilizes an attention mechanism to alleviate the effect of the initialization-sensitive nature of the searched network while enhancing the feature presentation ability. Extensive experiments on the Lung Image Database Consortium image collection (LIDC-IDRI) benchmark dataset show that the proposed method achieves a lung nodule classification accuracy of 92.93%, which is superior to the state-of-the-art methods.

中文翻译：

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A-DARTS：关注导向的可区分结构搜索，以进行肺结节分类

在过去的几年中，肺癌已导致最多的癌症死亡。良性-恶性肺结节分类对肺结节的检测至关重要，这有助于早期诊断肺癌。现有的大多数工作都使用设计良好的网络来提取胸部CT图像的特征，这需要专家的大量努力。为了自动化网络设计的手动过程，我们提出了一种注意力导向的可微体系结构搜索（A-DARTS）方法，该方法可直接在胸部CT图像上搜索最佳网络。另外，A-DARTS利用注意力机制来减轻搜索网络初始化敏感特性的影响，同时增强特征表示能力。