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.

本研究旨在设计基于 3D 神经网络、融合时空特征的 SlowFast 网络以及增强噪声鲁棒性的 SwAV 方法的计算机辅助诊断系统，以此区分良性和恶性肺部病变，协助医生进行径向探针支气管内超声检查视频的实时分析，且无需进行高质量图像选择，并在图像噪声存在的情况下实现高准确率分类诊断。