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。

使用自监督学习的时空双流网络对径向探针支气管内超声视频中的肺部肿瘤进行分类

Using Spatio-Temporal Dual-Stream Network with Self-Supervised Learning  for Lung Tumor Classification on Radial Probe Endobronchial Ultrasound Video

We propose a novel 3D neural network architecture for 3D hand pose estimation
from a single depth image. Different from previous works that mostly run on 2D
depth image domain and require intermediate or post process to bring in the
supervision from 3D space, we convert the depth map to a 3D volumetric
representation, and feed it into a 3D convolutional neural network(CNN) to
directly produce the pose in 3D requiring no further process. Our system does
not require the ground truth reference point for initialization, and our
network architecture naturally integrates both local feature and global context
in 3D space. To increase the coverage of the hand pose space of the training
data, we render synthetic depth image by transferring hand pose from existing
real image datasets. We evaluation our algorithm on two public benchmarks and
achieve the state-of-the-art performance. The synthetic hand pose dataset will
be available.

使用新的 3D 神经网络架构，通过深度图到三维体积表示的转化，直接对手的三维姿态进行估计，无需进一步处理。同时通过合成深度图来增加训练数据覆盖率，在公开数据上达到了最优性能。