Uncertainty Global Contrastive Learning Framework for Semi-Supervised Medical Image Segmentation.

IF 6.7 2区 医学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-11-06 DOI:10.1109/JBHI.2024.3492540
Hengyang Liu, Pengcheng Ren, Yang Yuan, Chengyun Song, Fen Luo
{"title":"Uncertainty Global Contrastive Learning Framework for Semi-Supervised Medical Image Segmentation.","authors":"Hengyang Liu, Pengcheng Ren, Yang Yuan, Chengyun Song, Fen Luo","doi":"10.1109/JBHI.2024.3492540","DOIUrl":null,"url":null,"abstract":"<p><p>In semi-supervised medical image segmentation, the issue of fuzzy boundaries for segmented objects arises. With limited labeled data and the interaction of boundaries from different segmented objects, classifying segmentation boundaries becomes challenging. To mitigate this issue, we propose an uncertainty global contrastive learning (UGCL) framework. Specifically, we propose a patch filtering method and a classification entropy filtering method to provide reliable pseudo-labels for unlabelled data, while separating fuzzy boundaries and high-entropy pixel points as unreliable points. Considering that unreliable regions contain rich complementary information, we introduce an uncertainty global contrast learning method to distinguish these challenging unreliable regions, enhancing intra-class compactness and inter-class separability at the global data level. Within our optimization framework, we also integrate consistency regularization techniques and select unreliable points as targets for consistency. As demonstrated, the contrastive learning and consistency regularization applied to uncertain points enable us to glean valuable semantic information from unreliable data, which enhances segmentation accuracy. We evaluate our method on two publicly available medical image datasets and compare it with other state-of-the-art semi-supervised medical image segmentation methods, and a series of experimental results show that our method has achieved substantial improvements.</p>","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":null,"pages":null},"PeriodicalIF":6.7000,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Journal of Biomedical and Health Informatics","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1109/JBHI.2024.3492540","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0

Abstract

In semi-supervised medical image segmentation, the issue of fuzzy boundaries for segmented objects arises. With limited labeled data and the interaction of boundaries from different segmented objects, classifying segmentation boundaries becomes challenging. To mitigate this issue, we propose an uncertainty global contrastive learning (UGCL) framework. Specifically, we propose a patch filtering method and a classification entropy filtering method to provide reliable pseudo-labels for unlabelled data, while separating fuzzy boundaries and high-entropy pixel points as unreliable points. Considering that unreliable regions contain rich complementary information, we introduce an uncertainty global contrast learning method to distinguish these challenging unreliable regions, enhancing intra-class compactness and inter-class separability at the global data level. Within our optimization framework, we also integrate consistency regularization techniques and select unreliable points as targets for consistency. As demonstrated, the contrastive learning and consistency regularization applied to uncertain points enable us to glean valuable semantic information from unreliable data, which enhances segmentation accuracy. We evaluate our method on two publicly available medical image datasets and compare it with other state-of-the-art semi-supervised medical image segmentation methods, and a series of experimental results show that our method has achieved substantial improvements.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
用于半监督医学图像分割的不确定性全局对比学习框架
在半监督医学图像分割中,出现了分割对象边界模糊的问题。由于标注数据有限,而且不同分割对象的边界相互影响,分割边界的分类变得极具挑战性。为了缓解这一问题,我们提出了不确定性全局对比学习(UGCL)框架。具体来说,我们提出了一种补丁过滤方法和一种分类熵过滤方法,为无标签数据提供可靠的伪标签,同时将模糊边界和高熵像素点分离为不可靠点。考虑到不可靠区域包含丰富的互补信息,我们引入了一种不确定性全局对比学习方法来区分这些具有挑战性的不可靠区域,从而在全局数据层面增强类内紧凑性和类间可分性。在优化框架内,我们还整合了一致性正则化技术,并选择不可靠点作为一致性目标。正如我们所展示的那样,应用于不确定点的对比学习和一致性正则化使我们能够从不可靠的数据中收集有价值的语义信息,从而提高分割的准确性。我们在两个公开的医学影像数据集上评估了我们的方法,并将其与其他最先进的半监督医学影像分割方法进行了比较,一系列实验结果表明,我们的方法取得了实质性的改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
IEEE Journal of Biomedical and Health Informatics
IEEE Journal of Biomedical and Health Informatics COMPUTER SCIENCE, INFORMATION SYSTEMS-COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
CiteScore
13.60
自引率
6.50%
发文量
1151
期刊介绍: IEEE Journal of Biomedical and Health Informatics publishes original papers presenting recent advances where information and communication technologies intersect with health, healthcare, life sciences, and biomedicine. Topics include acquisition, transmission, storage, retrieval, management, and analysis of biomedical and health information. The journal covers applications of information technologies in healthcare, patient monitoring, preventive care, early disease diagnosis, therapy discovery, and personalized treatment protocols. It explores electronic medical and health records, clinical information systems, decision support systems, medical and biological imaging informatics, wearable systems, body area/sensor networks, and more. Integration-related topics like interoperability, evidence-based medicine, and secure patient data are also addressed.
期刊最新文献
Front Cover Table of Contents Estimation and Conformity Evaluation of Multi-Class Counterfactual Explanations for Chronic Disease Prevention. Hierarchical graph representation learning with multi-granularity features for anti-cancer drug response prediction. Uncertainty Global Contrastive Learning Framework for Semi-Supervised Medical Image Segmentation.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1