Tekchandani Hitesh, Verma Shrish, Londhe Narendra
Electronics and Communication Engineering, National Institute of Technology Raipur, NIT Raipur, G E Road, Raipur, Chhattisgarh 492010, India.
Electrical Engineering, National Institute of Technology Raipur, NIT Raipur,G E Road, Raipur, Chhattisgarh 492010, India.
Comput Methods Programs Biomed. 2020 Oct;194:105478. doi: 10.1016/j.cmpb.2020.105478. Epub 2020 May 22.
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.
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.
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.
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.
在肺癌中，纵隔淋巴结（MLN）状态为良性或恶性的判定会影响治疗方案的制定和生存率。将MLN分类为良性和恶性的侵入性病理检查存在各种缺点，如疼痛、与麻醉相关的风险，并且在很大程度上取决于进行检查的外科医生的技能和偏好。因此，研究人员广泛探索了用于MLN严重程度检测的计算机辅助系统。最近，在我们早期完成的关于计算机断层扫描（CT）图像中MLN鉴别诊断的非侵入性方法的工作中，实施了不同的数据增强方法和先进的全卷积网络（FCN）的组合，以提高恶性肿瘤检测的性能。然而，FCN网络的性能高度依赖于适当的数据增强方法的选择及其超参数的控制。此外，在卷积神经网络（CNN）模型中获取分层特征的标准做法需要更深的层堆叠。这导致可训练参数数量增加，并容易导致网络过拟合。
鉴于上述局限性，在本文中，作者提出了一种方法，该方法包括：1）用于数据增强的生成对抗网络（GAN），以及2）用于恶性肿瘤检测的Inception网络。与传统的数据增强策略不同，基于GAN的增强方法生成与原始数据分布相关的数据。在基于Inception的模型中，它使用具有分解卷积的多个大小的内核进行分层特征提取。这有助于显著减少可训练参数和过拟合问题。
在本文中，进行了不同GAN方法以及不同Inception架构的实验，分别用于评估和验证为MLN严重程度检测选择合适的GAN和Inception架构。所提出的方法取得了优异的结果，平均准确率、灵敏度、特异性和曲线下面积分别为94.95%、93.65%、96.67%和95%。
获得的结果验证了GAN在良性和恶性MLN鉴别诊断中用于数据增强的有效性。与各种文献中提出的所有研究方法相比，所提出的基于Inception网络的恶性肿瘤检测分类器显示出有前景的结果。
