融合片内语义和片间结构特征的自监督CT图像分类方法OA

In view of the scarcity of artificial labels and poor classification performance in CT(Computed Tomo-graphy)image analysis,a self-supervised CT image classification method combining in-slice semantic and inter-slice structural features is proposed in this study.In this method,the hierarchical structure of CT images and the se-mantic features of local components are utilized to process the unlabeled lesion images through the confusion section generation algorithm,and the spatial index and confusion section are generated as supervisory information.In the self-supervised auxiliary task,the ResNet50 network was used to extract both the intraslice semantic and interslice structural features related to the lesion site from the confused sections,and the learned features were transferred to the subsequent medical classification task,so that the final model gained from the unlabeled data.The experimental re-sults show that compared with other 2D and 3D models for CT images,the proposed method can achieve better classi-fication performance and label utilization efficiency when the used labeled data is limited.