Combining multi-scale feature fusion with multi-attribute grading, a CNN model for benign and malignant classification of pulmonary nodules
Combining multi-scale feature fusion with multi-attribute grading, a CNN model for benign and malignant classification of pulmonary nodules
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DOI：
10.1007/s10278-020-00333-1
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发表时间：
2020-04
影响因子：
4.4
通讯作者：
Jumin Zhao;Chen Zhang;Deng-ao Li;Jing Niu
中科院分区：
文献类型：
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作者：
Jumin Zhao;Chen Zhang;Deng-ao Li;Jing Niu
肺癌是所有癌症中死亡率最高的，及早发现可以提高存活率。近年来，低剂量CT被广泛应用于肺癌的检测。然而，诊断受到医生主观经验的限制。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，其中良性肿瘤554例，恶性肺结节450例。根据医生对结节中心坐标的注释，提取出两个不同尺度的肺结节三维CT图像斑块。在本研究中，我们的工作主要集中在两个方面。首先，首次构建了多尺度特征与多属性分类相结合的多流多任务网络，并将其应用于肺结节的良恶性分类。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。最后的实验结果表明，与同类研究相比，该模型是有效的。ROC曲线下面积为0.979，准确度为93.92%，敏感度为92.60%，特异度为96.25%。
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.