用双网络+对比学习，减少医学影像分割对标注数据的依赖。
Semi-Supervised Medical Image Segmentation via Dual Networks
- 双网络架构融合上下文信息，提升伪标签可靠性。
- 在少量标注数据下，3D MRI分割性能超越现有方法。
- 适合医疗图像标注稀缺场景，尤其适合科研与临床落地。
传统监督式医学图像分割需大量标注数据，但现实中获取困难。现有半监督方法仍受伪标签噪声和特征空间监督不足影响。本文提出一种创新的3D半监督医学图像分割方法，降低对大规模标注数据的依赖。通过双网络架构增强上下文信息利用，生成更可靠的伪标签；结合自监督对比学习策略，提升网络表征能力，区分可靠与不可靠预测，降低预测不确定性。在临床磁共振成像数据上的实验表明，该方法优于当前最优技术。代码已开源：https://github.com/AIPMLab/Semi-supervised-Segmentation。
原文摘要 · Abstract (English)
Traditional supervised medical image segmentation models require large amounts of labeled data for training; however, obtaining such large-scale labeled datasets in the real world is extremely challenging. Recent semi-supervised segmentation models also suffer from noisy pseudo-label issue and limited supervision in feature space. To solve these challenges, we propose an innovative semi-supervised 3D medical image segmentation method to reduce the dependency on large, expert-labeled datasets. Furthermore, we introduce a dual-network architecture to address the limitations of existing methods in using contextual information and generating reliable pseudo-labels. In addition, a self-supervised contrastive learning strategy is used to enhance the representation of the network and reduce prediction uncertainty by distinguishing between reliable and unreliable predictions. Experiments on clinical magnetic resonance imaging demonstrate that our approach outperforms state-of-the-art techniques. Our code is available at https://github.com/AIPMLab/Semi-supervised-Segmentation.
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