Background and objective

In lung cancer, the determination of mediastinal lymph node (MLN) status as benign or malignant influence treatment planning and survival rate. Invasive pathological tests for the classification of MLNs into benign and malignant have various shortcomings like painfulness, the risk associated with anesthesia, and depends to a large extent on skillset and preferences of the surgeon performing the test. Hence, computer-aided system for MLNs severity detection has been explored widely by the researchers. Very recently, in our earlier concluded work on non-invasive method for MLNs differential diagnosis in computed tomography (CT) images, combination of different data augmentation approaches and state-of-art fully convolutional network (FCN) were implemented to enhance the performance of malignancy detection. However, the performance of FCN network were highly depended on the selection of appropriate data augmentation approach and control of their hyperparameters. Moreover, a standard practice to get hierarchical features in convolutional neural network (CNN) models requires deeper stacking of layers. This leads to an increase in number of trainable parameters which prone to overfitting of the network.

Methods

In view of the above mention limitations, in this paper, authors have proposed an approach that includes: 1) Generative Adversarial Network (GAN) for data augmentation, and 2) Inception network for malignancy detection. Unlike conventional data augmentation strategy, GAN based augmentation approach generates data that correlates to original data distribution. In the case of Inception based model, it uses multiple size kernels with factorized convolution for hierarchical feature extraction. This helps to a significant reduction in trainable parameters and the problem of overfitting.

Results

In this paper, experiments with different GAN approaches, as well as with different Inception architectures, are conducted to evaluate and justify the selection of appropriate GAN and Inception architecture, respectively for MLNs severity detection. The proposed approach achieves superior results with an average accuracy, sensitivity, specificity, and area under curve of 94.95%, 93.65%, 96.67%, and 95%, respectively.

Conclusion

The obtained results validate the usefulness of GANs for data augmentation in the differential diagnosis of benign and malignant MLNs. The proposed Inception network based classifier for malignancy detection shows promising results compared to all investigated methods presented in various literature.

中文翻译：

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使用GAN和Inception网络提高纵隔淋巴结严重程度检测的性能。

背景和目标

在肺癌中，确定纵隔淋巴结（MLN）状态为良性还是恶性会影响治疗计划和生存率。用于将MLN分为良性和恶性的侵入性病理学检查具有多种缺点，例如疼痛，与麻醉相关的风险，并且在很大程度上取决于手术医师的技能和偏好。因此，研究人员已广泛探索了用于MLN严重性检测的计算机辅助系统。最近，在我们较早前完成的关于在计算机断层扫描（CT）图像中进行MLN鉴别诊断的非侵入性方法的工作中，将不同的数据增强方法和最新的全卷积网络（FCN）结合起来使用，以增强图像处理的性能。恶性肿瘤检测。然而，FCN网络的性能高度依赖于适当的数据扩充方法的选择及其超参数的控制。此外，在卷积神经网络（CNN）模型中获取层次结构特征的标准做法要求更深层次的堆叠。这导致可训练参数的数量增加，这易于网络的过度拟合。

方法

鉴于上述限制，作者提出了一种方法，该方法包括：1）生成数据的对抗网络（GAN）用于数据增强，以及2）恶性检测的先验网络。与常规数据增强策略不同，基于GAN的增强方法会生成与原始数据分布相关的数据。对于基于Inception的模型，它使用具有分解卷积的多个大小内核进行分层特征提取。这有助于显着减少可训练参数和过度拟合的问题。

结果

在本文中，使用不同的GAN方法以及不同的Inception体系结构进行了实验，以评估和证明分别用于MLN严重性检测的GAN和Inception体系结构的选择。所提出的方法以平均准确度，灵敏度，特异性和曲线下面积分别达到94.95％，93.65％，96.67％和95％达到了优异的结果。

结论

获得的结果验证了GAN在良性和恶性MLN的鉴别诊断中用于数据增强的有用性。与各种文献中提出的所有研究方法相比，基于提议的基于Inception网络的恶性检测分类器显示出令人鼓舞的结果。