This study aims to establish a computer-aided diagnostic system for lung
lesions using bronchoscope endobronchial ultrasound (EBUS) to assist physicians
in identifying lesion areas. During EBUS-transbronchial needle aspiration
(EBUS-TBNA) procedures, physicians rely on grayscale ultrasound images to
determine the location of lesions. However, these images often contain
significant noise and can be influenced by surrounding tissues or blood
vessels, making interpretation challenging. Previous research has lacked the
application of object detection models to EBUS-TBNA, and there has been no
well-defined solution for annotating the EBUS-TBNA dataset. In related studies
on ultrasound images, although models have been successful in capturing target
regions for their respective tasks, their training and predictions have been
based on two-dimensional images, limiting their ability to leverage temporal
features for improved predictions. This study introduces a three-dimensional
image-based object detection model. It utilizes an attention mechanism to
capture temporal correlations and we will implements a filtering mechanism to
select relevant information from previous frames. Subsequently, a
teacher-student model training approach is employed to optimize the model
further, leveraging unlabeled data. To mitigate the impact of poor-quality
pseudo-labels on the student model, we will add a special Gaussian Mixture
Model (GMM) to ensure the quality of pseudo-labels.

该研究旨在建立一个基于计算机辅助诊断系统的肺病变诊断方法，使用支气管镜内支气管超声（EBUS）来帮助医生识别病变区域。