Zhao Jumin, Zhang Chen, Li Dengao, Niu Jing
College of Information and Computer, Taiyuan University of Technology, Jinzhong, China.
Technology Research Center of Spatial Information Network Engineering of Shanxi, Jinzhong, China.
J Digit Imaging. 2020 Aug;33(4):869-878. doi: 10.1007/s10278-020-00333-1.
Lung cancer has the highest mortality rate of all cancers, and early detection can improve survival rates. In the recent years, low-dose CT has been widely used to detect lung cancer. However, the diagnosis is limited by the subjective experience of doctors. Therefore, the main purpose of this study is to use convolutional neural network to realize the benign and malignant classification of pulmonary nodules in CT images. We collected 1004 cases of pulmonary nodules from LIDC-IDRI dataset, among which 554 cases were benign and 450 cases were malignant. According to the doctors' annotates on the center coordinates of the nodules, two 3D CT image patches of pulmonary nodules with different scales were extracted. In this study, our work focuses on two aspects. Firstly, we constructed a multi-stream multi-task network (MSMT), which combined multi-scale feature with multi-attribute classification for the first time, and applied it to the classification of benign and malignant pulmonary nodules. Secondly, we proposed a new loss function to balance the relationship between different attributes. The final experimental results showed that our model was effective compared with the same type of study. The area under ROC curve, accuracy, sensitivity, and specificity were 0.979, 93.92%, 92.60%, and 96.25%, respectively.
肺癌在所有癌症中死亡率最高，早期检测可提高生存率。近年来，低剂量CT已被广泛用于肺癌检测。然而，诊断受医生主观经验限制。因此，本研究的主要目的是利用卷积神经网络实现CT图像中肺结节的良恶性分类。我们从LIDC-IDRI数据集中收集了1004例肺结节病例，其中554例为良性，450例为恶性。根据医生对结节中心坐标的标注，提取了两个不同尺度的肺结节3D CT图像块。在本研究中，我们的工作集中在两个方面。首先，我们构建了一个多流多任务网络（MSMT），首次将多尺度特征与多属性分类相结合，并将其应用于肺结节良恶性分类。其次，我们提出了一种新的损失函数来平衡不同属性之间的关系。最终实验结果表明，与同类研究相比，我们的模型是有效的。ROC曲线下面积、准确率、灵敏度和特异性分别为0.979、93.92%、92.60%和96.25%。
