电子科技2024，Vol.37Issue(7)：43-52,10.DOI:10.16180/j.cnki.issn1007-7820.2024.07.006
融合片内语义和片间结构特征的自监督CT图像分类方法
A Self-Supervised CT Image Classification Method Incorporating Intra-Slice Semantic and Inter-Slice Structural Features
摘要
Abstract
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.关键词
医学图像分类/三维医学图像处理/CT图像/自监督学习/迁移学习/小样本学习/片内语义特征/片间结构特征/ResNet50Key words
medical image classification/3D medical image processing/CT images/self-supervised learning/transfer learning/few shot learning/intra-slice semantic features/inter-slice structural features/ResNet50分类
信息技术与安全科学引用本文复制引用
曹春萍,许志华..融合片内语义和片间结构特征的自监督CT图像分类方法[J].电子科技,2024,37(7):43-52,10.基金项目
浙江省卫生健康委员会面上项目(2022KY122) （2022KY122）
浙江省中医药科技计划(2019ZA023)Zhejiang Health and Wellness Commission Facially Project(2022KY122) （2019ZA023）
Zhejiang TCM Science and Technology Program(2019ZA023) （2019ZA023）
