The purpose of this study is to develop a computer-aided diagnosis system for classifying benign and malignant lung lesions, and to assist physicians in real-time analysis of radial probe endobronchial ultrasound (EBUS) videos. During the biopsy process of lung cancer, physicians use real-time ultrasound images to find suitable lesion locations for sampling. However, most of these images are difficult to classify and contain a lot of noise. Previous studies have employed 2D convolutional neural networks to effectively differentiate between benign and malignant lung lesions, but doctors still need to manually select good-quality images, which can result in additional labor costs. In addition, the 2D neural network has no ability to capture the temporal information of the ultrasound video, so it is difficult to obtain the relationship between the features of the continuous images. This study designs an automatic diagnosis system based on a 3D neural network, uses the SlowFast architecture as the backbone to fuse temporal and spatial features, and uses the SwAV method of contrastive learning to enhance the noise robustness of the model. The method we propose includes the following advantages, such as (1) using clinical ultrasound films as model input, thereby reducing the need for high-quality image selection by physicians, (2) high-accuracy classification of benign and malignant lung lesions can assist doctors in clinical diagnosis and reduce the time and risk of surgery, and (3) the capability to classify well even in the presence of significant image noise. The AUC, accuracy, precision, recall and specificity of our proposed method on the validation set reached 0.87, 83.87%, 86.96%, 90.91% and 66.67%, respectively. The results have verified the importance of incorporating temporal information and the effectiveness of using the method of contrastive learning on feature extraction.

翻译：本研究旨在开发一种计算机辅助诊断系统，用于分类肺部良恶性病变，并辅助医生实时分析径向探头支气管内超声（EBUS）视频。在肺癌活检过程中，医生使用实时超声图像寻找适合取样的病变位置。然而，这些图像大多难以分类且包含大量噪声。以往研究采用二维卷积神经网络有效区分肺部良恶性病变，但医生仍需手动筛选高质量图像，这可能导致额外人力成本。此外，二维神经网络无法捕捉超声视频的时间信息，难以获取连续图像特征之间的关系。本研究设计了一种基于三维神经网络的自动诊断系统，采用SlowFast架构作为骨干网络融合时空特征，并利用对比学习的SwAV方法增强模型的噪声鲁棒性。我们提出的方法具有以下优势：（1）以临床超声影像作为模型输入，从而减少医生对高质量图像筛选的需求；（2）对肺部良恶性病变的高精度分类可辅助医生进行临床诊断，减少手术时间和风险；（3）即使在存在显著图像噪声的情况下仍能良好分类。该方法在验证集上的AUC、准确率、精确率、召回率和特异度分别达到0.87、83.87%、86.96%、90.91%和66.67%。结果验证了融入时间信息的重要性，以及在特征提取中采用对比学习方法的有效性。