Lung cancer is the leading cause of death among cancer-related death. Like other cancers, the finest solution for lung cancer diagnosis and treatment is early screening. Automatic CAD system of lung cancer screening from Computed Tomography scan mainly involves two steps: detect all suspicious pulmonary nodules and evaluate the malignancy of the nodules. Recently, there are many works about the first step, but rare about the second step. Since the presence of pulmonary nodules does not absolutely specify cancer, the morphology of nodules such as shape, size, and contextual information has a sophisticated relationship with cancer, the screening of lung cancer needs a careful investigation on each suspicious nodule and integration of information of all nodules. We propose deep CNN architecture which differs from those traditionally used in computer vision to solve this problem. First, the suspicious nodules are generated with the modified version of U-Net and then the generated nodules become an input data for our model. The proposed model is a multi-path CNN which exploits both local features as well as more global contextual features simultaneously to automatically detect lung cancer. To this end, the model used three paths, each path employed different receptive field size which helps to model distant dependencies (short and long-range dependencies of the neighboring pixels). Then, to further upgrade our model performance, we concatenate features from the three paths. This balance the receptive field size effect and makes our model more adaptable to the variability of shape, size, and contextual information among nodules. Finally, we also introduce a retraining phase system that permits us to tackle difficulties related to the imbalance of image labels. Experimental results on Kaggle Data Science Bowl 2017 challenge shows that our model is better adaptable to the described inconsistency among nodules size and shape, and also obtained better detection results compared to the recently published state of the art methods.

中文翻译：

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用于肺癌检测的多路径卷积神经网络

肺癌是癌症相关死亡中的首要死因。与其他癌症一样，肺癌诊断和治疗的最佳解决方案是早期筛查。计算机断层扫描肺癌筛查的自动CAD系统主要包括两个步骤：检测所有可疑肺结节和评估结节的恶性程度。最近关于第一步的作品很多，关于第二步的作品却很少。由于肺结节的存在并不能绝对确定癌症，结节的形状、大小、上下文信息等形态与癌症有着复杂的关系，肺癌的筛查需要对每个可疑结节进行仔细调查，并整合相关信息。所有结节。我们提出了不同于传统计算机视觉中使用的深度 CNN 架构来解决这个问题。首先，使用 U-Net 的修改版本生成可疑结节，然后生成的结节成为我们模型的输入数据。所提出的模型是一个多路径 CNN，它同时利用局部特征和更全局的上下文特征来自动检测肺癌。为此，该模型使用了三个路径，每条路径采用不同的感受野大小，这有助于模拟远距离依赖关系（相邻像素的短距离和长距离依赖关系）。然后，为了进一步提升我们的模型性能，我们将三个路径的特征连接起来。这平衡了感受野大小效应，使我们的模型更适应形状、大小、和结节之间的上下文信息。最后，我们还引入了一个再训练阶段系统，它允许我们解决与图像标签不平衡相关的困难。Kaggle Data Science Bowl 2017 挑战赛的实验结果表明，与最近发表的最先进方法相比，我们的模型更好地适应了所描述的结节大小和形状之间的不一致性，并且还获得了更好的检测结果。