题名 | Leveraging CORAL-Correlation Consistency Network for Semi-Supervised Left Atrium MRI Segmentation |
作者 | |
发表日期 | 2024 |
会议名称 | 2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024 |
会议录名称 | Proceedings - 2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024
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页码 | 3434-3438 |
会议日期 | December 3-6, 2024 |
会议地点 | Lisbon |
摘要 | Semi-supervised learning (SSL) has been widely used to learn from both a few labeled images and many unlabeled images to overcome the scarcity of labeled samples in medical image segmentation. Most current SSL-based segmentation methods use pixel values directly to identify similar features in labeled and unlabeled data. They usually fail to accurately capture the intricate attachment structures in the left atrium, such as the areas of inconsistent density or exhibit outward curvatures, adding to the complexity of the task. In this paper, we delve into this issue and introduce an effective solution, CORAL(Correlation-Aligned)-Correlation Consistency Network (CORN), to capture the global structure shape and local details of Left Atrium. Diverging from previous methods focused on each local pixel value, the CORAL-Correlation Consistency Module (CCM) in the CORN leverages second-order statistical information to capture global structural features by minimizing the distribution discrepancy between labeled and unlabeled samples in feature space. Yet, direct construction of features from unlabeled data frequently results in "Sample Selection Bias", leading to flawed supervision. We thus further propose the Dynamic Feature Pool (DFP) for the CCM, which utilizes a confidence-based filtering strategy to remove incorrectly selected features and regularize both teacher and student models by constraining the similarity matrix to be consistent. Extensive experiments on the Left Atrium dataset have shown that the proposed CORN outperforms previous state-of-the-art semi-supervised learning methods. |
关键词 | CORAL-Correlation Left Atrium Medical Image Segmentation Semi-supervised learning |
DOI | 10.1109/BIBM62325.2024.10822694 |
URL | 查看来源 |
语种 | 英语English |
Scopus入藏号 | 2-s2.0-85217278636 |
引用统计 | |
文献类型 | 会议论文 |
条目标识符 | https://repository.uic.edu.cn/handle/39GCC9TT/13084 |
专题 | 理工科技学院 |
通讯作者 | Su, Weifeng |
作者单位 | 1.BNU-HKBU United International College,Department of Computer Science,China 2.Hong Kong Baptist University,Hong Kong 3.Guangdong Provincial Key Laboratory of Irads,China 4.School of Humanities,Central South University,China |
第一作者单位 | 北师香港浸会大学 |
通讯作者单位 | 北师香港浸会大学 |
推荐引用方式 GB/T 7714 | Li, Xinze,Huang, Runlin,Wu, Zhenghaoet al. Leveraging CORAL-Correlation Consistency Network for Semi-Supervised Left Atrium MRI Segmentation[C], 2024: 3434-3438. |
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