计算机科学
分割
人工智能
一致性(知识库)
背景(考古学)
任务(项目管理)
模式识别(心理学)
班级(哲学)
代表(政治)
注释
特征提取
特征(语言学)
图像分割
编码(集合论)
图像(数学)
政治学
政治
语言学
法学
程序设计语言
管理
集合(抽象数据类型)
经济
生物
古生物学
哲学
作者
Jingkun Chen,Jianguo Zhang,Kurt Debattista,Jungong Han
出处
期刊:IEEE Transactions on Medical Imaging
[Institute of Electrical and Electronics Engineers]
日期:2023-03-01
卷期号:42 (3): 594-605
被引量:22
标识
DOI:10.1109/tmi.2022.3213372
摘要
Deep learning-based semi-supervised learning (SSL) algorithms are promising in reducing the cost of manual annotation of clinicians by using unlabelled data, when developing medical image segmentation tools. However, to date, most existing semi-supervised learning (SSL) algorithms treat the labelled images and unlabelled images separately and ignore the explicit connection between them; this disregards essential shared information and thus hinders further performance improvements. To mine the shared information between the labelled and unlabelled images, we introduce a class-specific representation extraction approach, in which a task-affinity module is specifically designed for representation extraction. We further cast the representation into two different views of feature maps; one is focusing on low-level context, while the other concentrates on structural information. The two views of feature maps are incorporated into the task-affinity module, which then extracts the class-specific representations to aid the knowledge transfer from the labelled images to the unlabelled images. In particular, a task-affinity consistency loss between the labelled images and unlabelled images based on the multi-scale class-specific representations is formulated, leading to a significant performance improvement. Experimental results on three datasets show that our method consistently outperforms existing state-of-the-art methods. Our findings highlight the potential of consistency between class-specific knowledge for semi-supervised medical image segmentation. The code and models are to be made publicly available at https://github.com/jingkunchen/TAC .
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